Similar Journals
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IEEE Robotics and Automation Letters
Number of Followers: 9  Hybrid journal (It can contain Open Access articles)ISSN (Online) 2377-3766 Published by IEEE  [228 journals]
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- Estimation of Gait Phase of Human Stair Descent Walking Based on Phase
Variable Approach-
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Authors: Myeongju Cha;Pilwon Hur;
Pages: 7691 - 7698
Abstract: Synchronization between a wearer and a lower limb powered prosthesis is important for effective control. Typically, phase variable-based phase estimation methods are employed. However, there is a noticeable lack of studies focusing on estimating the gait phase during stair descent, likely due to the difficulty in generating a reliable phase variable. In most studies, the thigh angle is used to generate phase variables for level walking because it follows a sinusoidal pattern. However, during stair descent, the thigh angle exhibits only a partially sinusoidal shape, making it challenging to apply the methods used for level walking. In this study, we propose a novel phase variable generation method to address the difficulty of using only the thigh angle for stair descent. To estimate the gait phase reliably, the phase variable is defined differently for the stance and swing phases: the hip position is used to generate the phase variable during the stance phase, and the thigh angle is used during the swing phase. These phase variables are then unified into a single phase variable (PV-ENT) for the entire gait cycle of stair descent. During this unification process, a non-smooth transition occurs around the phase transition point. To address this, a blending method is applied. The proposed method was validated using the data from 12 healthy subjects, collected through a motion capture system and IMU sensors. The results demonstrate a reliable phase estimation performance. Moreover, the blending method successfully improves the smoothness of the phase variable around the phase transition point without reducing the overall phase estimation performance.
PubDate: THU, 12 JUN 2025 09:17:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- EMOTION: Expressive Motion Sequence Generation for Humanoid Robots With
In-Context Learning-
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Authors: Peide Huang;Yuhan Hu;Nataliya Nechyporenko;Daehwa Kim;Walter Talbott;Jian Zhang;
Pages: 7699 - 7706
Abstract: This letter introduces a framework, called EMOTION, for generating expressive motion sequences in humanoid robots, enhancing their ability to engage in human-like non-verbal communication. Non-verbal cues such as facial expressions, gestures, and body movements play a crucial role in effective interpersonal interactions. Despite the advancements in robotic behaviors, existing methods often fall short in mimicking the diversity and subtlety of human non-verbal communication. To address this gap, our approach leverages the in-context learning capability of large language models (LLMs) to dynamically generate socially appropriate gesture motion sequences for human-robot interaction. We use this framework to generate 10 different expressive gestures and conduct online user studies comparing the naturalness and understandability of the motions generated by EMOTION and its human-feedback version, EMOTION++, against those by human operators. The results demonstrate that our approach either matches or surpasses human performance in generating understandable and natural robot motions under certain scenarios. We also provide design implications for future research to consider a set of variables when generating expressive robotic gestures.
PubDate: MON, 02 JUN 2025 09:18:37 -04
Issue No: Vol. 10, No. 8 (2025)
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- Occupation Point Planning and Tracking Control of an Underactuated
Multi-Robot System to Capture a Fast Evader-
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Authors: Haiyan Zhao;Rongxin Cui;Weisheng Yan;
Pages: 7707 - 7714
Abstract: This letter presents a cooperative mechanism for capturing a fast evader in a 2D space with obstacles, using a multi-robot system with a positive capture radius. First, we define the dominance region with a Cartesian oval, parameterized by the speed ratio and capture radius, and derive the minimum number of pursuers required for successful capture. Second, we develop explicit strategies to construct a defense manifold and a coverage mapping rule for occupation points, ensuring that pursuers maintain and shrink the defensible area until the evader is captured. Finally, we design a state-feedback control law for the pursuers described by second-order nonlinear underactuated dynamics, enabling finite-time tracking of occupation points while avoiding collisions and exposure. The proposed method is also applicable to heterogeneous scenarios with both high- and low-speed pursuers. Simulations and experiments with ground mobile robots validate the effectiveness of our approach.
PubDate: FRI, 13 JUN 2025 09:17:10 -04
Issue No: Vol. 10, No. 8 (2025)
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- A Reinforcement Learning Approach to Non-Prehensile Manipulation Through
Sliding-
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Authors: Hamidreza Raei;Elena De Momi;Arash Ajoudani;
Pages: 7715 - 7722
Abstract: Although robotic applications increasingly demand versatile and dynamic object handling, most existing techniques are predominantly focused on grasp-based manipulation, limiting their applicability in non-prehensile tasks. To address this need, this study introduces a Deep Deterministic Policy Gradient (DDPG) reinforcement learning (RL) framework for efficient non-prehensile manipulation, specifically for sliding an object on a surface. The algorithm generates a linear trajectory by precisely controlling the acceleration of a robotic arm rigidly coupled to the horizontal surface, enabling the relative manipulation of an object as it slides along the surface. Furthermore, two distinct algorithms have been developed to estimate the frictional forces dynamically during the sliding process. These algorithms dynamically provide friction estimates online after each action, serving as critical feedback to the actor model. This feedback mechanism enhances the policy's adaptability and robustness, ensuring more precise control of the platform's acceleration in response to varying surface conditions. The proposed algorithm is validated through simulations and real-world experiments. Results demonstrate that the proposed framework effectively generalizes sliding manipulation across varying distances and, more importantly, adapts to different surfaces with diverse frictional properties. Notably, the trained model exhibits zero-shot sim-to-real transfer capabilities.
PubDate: MON, 09 JUN 2025 09:17:11 -04
Issue No: Vol. 10, No. 8 (2025)
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- “Feariosity”-Guided Reinforcement Learning for Safe and Efficient
Autonomous End-to-End Navigation-
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Authors: Dong Hu;Longfei Mo;Jingda Wu;Chao Huang;
Pages: 7723 - 7730
Abstract: End-to-end navigation strategies using reinforcement learning (RL) can improve the adaptability and autonomy of Autonomous ground vehicles (AGVs) in complex environments. However, RL still faces challenges in data efficiency and safety. Neuroscientific and psychological research shows that during exploration, the brain balances between fear and curiosity, a critical process for survival and adaptation in dangerous environments. Inspired by this scientific insight, we propose the “Feariosity” model, which integrates fear and curiosity model to simulate the complex psychological dynamics organisms experience during exploration. Based on this model, we developed an innovative policy constraint method that evaluates potential hazards and applies necessary safety constraints while encouraging exploration of unknown areas. Additionally, we designed a new experience replay mechanism that quantifies the threat and unknown level of data, optimizing their usage probability. Extensive experiments in both simulation and real-world scenarios demonstrate that the proposed method significantly improves data efficiency, asymptotic performance during training. Furthermore, it achieves higher success rates, driving efficiency, and robustness in deployment. This also highlights the key role of mimicking biological neural and psychological mechanisms in improving the safety and efficiency through RL.
PubDate: FRI, 06 JUN 2025 09:17:27 -04
Issue No: Vol. 10, No. 8 (2025)
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- Learning Fast, Tool-Aware Collision Avoidance for Collaborative Robots
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Authors: Joonho Lee;Yunho Kim;Seokjoon Kim;Quan Nguyen;Youngjin Heo;
Pages: 7731 - 7738
Abstract: Ensuring safe and efficient operation of collaborative robots in human environments is challenging, especially in dynamic settings where both obstacle motion and tasks change over time. Current robot controllers typically assume full visibility and fixed tools, which can lead to collisions or overly conservative behavior. In our work, we introduce a tool-aware collision avoidance system that adjusts in real time to different tool sizes and modes of tool-environment interaction. Using a learned perception model, our system filters out robot and tool components from the point cloud, reasons about occluded area, and predicts collision under partial observability. We then use a control policy trained via constrained reinforcement learning to produce smooth avoidance maneuvers in under 10 milliseconds. In simulated and real-world tests, our approach outperforms traditional approaches (APF, MPPI) in dynamic environments, while maintaining sub-millimeter accuracy. Moreover, our system operates with approximately 60% lower computational cost compared to a state-of-the-art GPU-based planner. Our approach provides modular, efficient, and effective collision avoidance for robots operating in dynamic environments. We integrate our method into a collaborative robot application and demonstrate its practical use for safe and responsive operation.
PubDate: THU, 12 JUN 2025 09:17:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- Jamming Metal Sheets Using Electropermanent Magnets for Stiffness
Modulation-
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Authors: Leah T. Gaeta;Vi T. Vo;Sang-Yoep Lee;Srushti Raste;Megha Venkatesam;Jacob Rogatinsky;M. Deniz Albayrak;Tommaso Ranzani;
Pages: 7739 - 7746
Abstract: Soft robots exhibit natural compliance which is desirable in many applications, but often require stiffness modulation techniques when more rigidity is needed. However, many existing stiffening techniques lack portability or fast response times, hindering the ubiquitous adoption of soft robots. Here we introduce a new rapid stiffness modulation method based on magnetism that exhibits portability due to electronic control. This technique jams together thin layers of inherently magnetic metal sheets with a magnetic field generated by electropermanent magnets (EPMs), producing rapid stiffness changes. Quasi-static and dynamic mechanical characterizations for samples with varied layer numbers are presented, highlighting how the magnetic attraction generated by EPMs can be exploited to create a jamming effect. Stiffness increases of up to 68% and energy absorptions of up to 113 mJ were found during quasi-static and dynamic characterizations, respectively. Finally, we demonstrate how this jamming technique can be used in a haptic feedback application and to play a miniaturized version of the game of Skee-Ball.
PubDate: WED, 11 JUN 2025 09:17:15 -04
Issue No: Vol. 10, No. 8 (2025)
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- TripleSolver: A Separate-Simultaneous-Separate Solving Framework for
Dual-Robot Calibration-
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Authors: Gumin Jin;Xingkai Yu;Yuqing Chen;Liangren Shi;Jianxun Li;
Pages: 7747 - 7754
Abstract: Calibrating unknown transformation relationships is crucial for achieving coordinated motion in dual-robot systems. This calibration process can be formulated by solving the transformation matrix equation $\mathbf {AXB=YCZ}$. Existing separate approaches handle the rotation and translation components of this equation sequentially, which may suffer from error propagation. In contrast, simultaneous approaches optimize both components together but often encounter instability due to challenges in balancing their weights. To tackle these issues, this letter introduces TripleSolver, a novel separate-simultaneous-separate solving framework to address $\mathbf {AXB = YCZ}$ through a three-stage process. First, the rotation and translation components are solved separately, yielding two sets of estimates. Second, the shared rotation parameters from these estimates are refined using simultaneous optimization with automatically calculated weights. Last, the remaining parameters are determined based on the refined rotation parameters. Comparative results against state-of-the-art methods indicate that the proposed framework offers efficient calibration with improved accuracy and robustness.
PubDate: MON, 16 JUN 2025 09:17:43 -04
Issue No: Vol. 10, No. 8 (2025)
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- Dynamics of Mental Models: Objective Vs. Subjective User Understanding of
a Robot in the Wild-
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Authors: Ferran Gebellí;Anaís Garell;Séverin Lemaignan;Raquel Ros;
Pages: 7755 - 7762
Abstract: In Human-Robot Interaction research, assessing how humans understand the robots they interact with is crucial, particularly when studying the impact of explainability and transparency. Some studies evaluate objective understanding by analysing the accuracy of users' mental models, while others rely on perceived, self-reported levels of subjective understanding. We hypothesise that both dimensions of understanding may diverge, thus being complementary methods to assess the effects of explainability on users. In our study, we track the weekly progression of the users' understanding of an autonomous robot operating in a healthcare centre over five weeks. Our results reveal a notable mismatch between objective and subjective understanding. In areas where participants lacked sufficient information, the perception of understanding, i.e. subjective understanding, raised with increased contact with the system while their actual understanding, objective understanding, did not. We attribute these results to inaccurate mental models that persist due to limited feedback from the system. Future research should clarify how both objective and subjective dimensions of understanding can be influenced by explainability measures, and how these two dimensions of understanding affect other desiderata such as trust or usability.
PubDate: THU, 12 JUN 2025 09:17:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- A Sequential Approach for Accurate Parameters Identification of Heavy-Duty
Hydraulic Manipulators Ensuring Physical Feasibility-
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Authors: Weidi Huang;Zhiwei Chen;Fu Zhang;Min Cheng;Ruqi Ding;Junhui Zhang;Bing Xu;
Pages: 7763 - 7770
Abstract: Accurate identification of dynamic parameters is essential for precise motion control and autonomous operation of heavy-duty hydraulic manipulators. However, due to their low-speed motion property, conventional approaches fail to simultaneously excite all parameters. To overcome this issue, a sequential parameter identification approach for heavy-duty hydraulic manipulators is proposed. All parameters are categorized based on their dynamic characteristic, and then distinct excitation trajectories have been designed to separately stimulate and identify each parameter. Dynamic parameters are fully excited, which is reflected in a reduced condition number of the observation matrix. Furthermore, an approach that ensures the physical feasibility of the identified parameters is constructed, which makes them more suitable for application in nonlinear control. The performance of the proposed method is evaluated with various identification methods, including traditional least squares, weighted least squares, and the method only considering physical feasibility. The results indicate a substantial decrease in torque prediction error compared to these methods. Specifically, the prediction accuracy of the joint torque using the proposed method has been improved by approximately 5.63% to 27.06%.
PubDate: THU, 12 JUN 2025 09:17:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- RoboMT: Human-Like Compliance Control for Assembly via a Bilateral Robotic
Teleoperation and Hybrid Mamba-Transformer Framework-
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Authors: Wang Rundong;Cheng Yanchun;Yuan Qilong;Prakash Alok;Francis EH Tay;Marcelo H. Ang;
Pages: 7771 - 7778
Abstract: Robotic compliance control is critical for delicate tasks such as electronic connector assembly, where precise force regulation and adaptability are paramount. However, traditional methods often struggle with modeling inaccuracies and sensor noise. Inspired by human adaptability in complex assembly operations, we present RoboMT, a novel framework that integrates a Mamba algorithm with a Transformer architecture to achieve human-like compliance control. By leveraging a bilateral teleoperation platform, we collect extensive real-time force/torque and motion data to form a comprehensive dataset for training. Furthermore, RoboMT incorporates an Adaptive Action Chunk module and a Temporal Fusion module to ensure smooth and robust action prediction. Experimental results across four electronic assembly tasks show that RoboMT achieves superior success rates (62–98%) over baselines (29–98%), while maintaining stable force regulation around 2.5 N, closely resembling human performance. During task transitions, RoboMT quickly stabilizes at 5 N with minimal overshoot, avoiding the large force spikes (over 24 N) seen in baselines. Additionally, RoboMT maintains an average inference speed of 55 ms per batch, balancing real-time responsiveness and control robustness. Overall, RoboMT presents a compelling pathway toward error-minimized, human-level compliance control, and generalization for real-world robotic assembly, setting a new benchmark for precision, adaptability, and robustness in robotic assembly.
PubDate: MON, 16 JUN 2025 09:17:43 -04
Issue No: Vol. 10, No. 8 (2025)
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- A Cross-Scale Manipulator Based on Magnetic-Driven Microwedges
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Authors: Minghao Yin;Tingting Bao;Xiaozhe You;Wenyue Guo;Jing Cui;Zhongyi Chu;
Pages: 7779 - 7786
Abstract: Thedimensions of components have recently expanded in range from the micrometer to centimeter scale in MEMS assembly, necessitating the regulation of adhesion force across a broad range to accommodate cross-scale micromanipulation tasks. Inspired by gecko, anisotropic microwedges can effectively regulate adhesion force by varying the contact area. On this basis, we propose a cross-scale manipulator based on magnetic-driven microwedges. Microwedges embedded with magnetic particles bend along the inclined direction in a unidirectional magnetic field, providing a larger adhesion area to realize pick-up tasks. While the magnetic field is driven in a dual directive mode, the interface between microwedges and the target is progressively disrupted, reducing the adhesion area until the place operation is completed. During the entire process, the actual adhesion area can be detected through microscopic vision to judge whether the pick-and-place conditions are met. Experiments indicate that the ratio of maximum and minimum adhesion force provided by the manipulator can reach 2335.2. The size of silicon wafers which can be put up and placed successfully is from 0.3 × 0.3 × 0.1 mm$^{3}$ to 3 × 3 × 0.4 mm$^{3}$, and the volume ratio between them can reach 400. Especially, the proposed manipulator can perform assembly tasks, which shows its stability and capability of cross-scale micromanipulation.
PubDate: MON, 09 JUN 2025 09:17:11 -04
Issue No: Vol. 10, No. 8 (2025)
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- Playing to the Strengths of High- and Low-Resolution Cues for Ultra-High
Resolution Image Segmentation-
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Authors: Qi Li;Jiexin Luo;Chunxiao Chen;Jiaxin Cai;Wenjie Yang;Yuanlong Yu;Shengfeng He;Wenxi Liu;
Pages: 7787 - 7794
Abstract: In ultra-high resolution image segmentation task for robotic platforms like AAVs and autonomous vehicles, existing paradigms process a downsampled input image through a deep network and the original high-resolution image through a shallow network, then fusing their features for final segmentation. Although these features are designed to be complementary, they often contain redundant or even conflicting semantic information, which leads to blurred edge contours, particularly for small objects. This is especially detrimental to robotics applications requiring precise spatial awareness. To address this challenge, we propose a novel paradigm that disentangles the task into two independent subtasks concerning high- and low-resolution inputs, leveraging high-resolution features exclusively to capture low-level structured details and low-resolution features for extracting semantics. Specifically, for the high-resolution input, we propose a region-pixel association experts scheme that partitions the image into multiple regions. For the low-resolution input, we assign compact semantic tokens to the partitioned regions. Additionally, we incorporate a high-resolution local perception scheme with an efficient field-enriched local context module to enhance small object recognition in case of incorrect semantic assignment. Extensive experiments demonstrate the state-of-the-art performance of our method and validate the effectiveness of each designed component.
PubDate: FRI, 13 JUN 2025 09:17:10 -04
Issue No: Vol. 10, No. 8 (2025)
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- Image-to-Force Estimation for Soft Tissue Interaction in Robotic-Assisted
Surgery Using Structured Light-
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Authors: Jiayin Wang;Mingfeng Yao;Yanran Wei;Xiaoyu Guo;Ayong Zheng;Weidong Zhao;
Pages: 7795 - 7802
Abstract: For Minimally Invasive Surgical (MIS) robots, accurate haptic interaction force feedback is essential for ensuring the safety of interacting with soft tissue. However, the majority of existing MIS robotic systems cannot facilitate direct measurement of the interaction force with hardware sensors due to space limitations. This letter introduces an effective vision-based scheme that utilizes a One-Shot structured light projection with a designed pattern on soft tissue coupled with haptic information processing through a trained image-to-force neural network. The images captured from the endoscopic stereo camera are analyzed to reconstruct high-resolution 3D point clouds for soft tissue deformation. The proposed methodology involves a modified PointNet-based force estimation method, which has demonstrated proficiency in accurately representing the intricate mechanical properties of soft tissue. To validate the efficacy of the proposed methodology, numerical force interaction experiments were conducted on three silicon materials with varying stiffness levels. The experimental results substantiate the efficacy of the proposed methodology.
PubDate: FRI, 13 JUN 2025 09:17:10 -04
Issue No: Vol. 10, No. 8 (2025)
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- A Study on Enhancing Wearer Adaptation Through Accurate Gait Phase
Prediction and Gradual Increase in Assistive Force Magnitude in Exosuits-
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Authors: Zhuo Wang;Chunjie Chen;Hui Chen;Sheng Wang;Jiale Zhang;Xiangyang Wang;Xinyu Wu;
Pages: 7803 - 7810
Abstract: Human-exosuit adaptation is a bi-directional process: exosuit-to-human locomotion adaptation maximizes the benefits of exosuit assistance, while human-to-exosuit adaptation accelerates the wearer's access to these benefits. To promote bi-directional adaptation, we investigated precise gait phase prediction and dynamic adjustment of force amplitude. For precise gait phase prediction, it was crucial to account for the impact of exosuit assistance on kinematics, as models trained on data collected with the exosuit assistance outperformed those trained without it. For dynamic adjustment of force amplitude, assistance progressively increased with each step as the wearer adapts, and we investigated two methods for progressively increasing the amplitude: Linear and Sigmoid. To evaluate these strategies, we conducted experiments with a soft exosuit assisting hip flexion, monitoring Root Mean Square variations of electromyographic signals from the Rectus Femoris muscle at each step. Compared to the constant amplitude approach, the gradually increasing amplitude approaches more effectively facilitated human-exosuit adaptation, with the Sigmoid model proving most effective. Specifically, with the Sigmoid growth model, wearers adapted to the exosuit after only 123$\pm$ 31 steps. This study underscored the importance of considering human-exosuit adaptation when introducing assistance and recommended a gradual increase in assistance for naive users.
PubDate: MON, 09 JUN 2025 09:17:11 -04
Issue No: Vol. 10, No. 8 (2025)
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- Rethinking Cross-Modal Interaction for Efficient Referring Image
Segmentation-
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Authors: Claudia Cuttano;Francesca Pistilli;Fabio Cermelli;Giuseppe Averta;
Pages: 7811 - 7818
Abstract: Referring Image Segmentation, the task of finding and segmenting objects in an image conditioned on a natural language description, is crucial for human-robot collaboration. However, current RIS methods often implement visual-text alignment relying on computationally intensive Transformer-based self-attention mechanisms, which impairs deployment on robots, especially those with limited computational resources. Indeed, beyond accuracy, practical robotic applications demand efficient models with small footprints. This letter introduces ERIS, an Efficient RIS approach designed for real-world deployment. ERIS achieves effective multi-modal interaction through a novel dual-branch architecture: a Visual Text Alignment branch and a Text Visual Refinement branch. This design implements bilateral alignment between textual and visual modalities without the computational burden of self-attention. Of note, the progressive alignment in ERIS enhances interpretability, revealing how textual cues guide segmentation. For the sake of efficiency, our alignment strategy produces structured embeddings which can be directly mapped into the final segmentation mask, without the need for additional segmentation heads. Thus, ERIS footprint scales linearly with respect to the number of visual and text tokens, making it suitable for both cloud-based and edge deployment. Experimental results demonstrate that ERIS achieves competitive or superior performance compared to state-of-the-art methods while significantly reducing computational cost, proving that efficiency and accuracy are not mutually exclusive.
PubDate: FRI, 13 JUN 2025 09:17:10 -04
Issue No: Vol. 10, No. 8 (2025)
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- TACT: Humanoid Whole-Body Contact Manipulation Through Deep Imitation
Learning With Tactile Modality-
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Authors: Masaki Murooka;Takahiro Hoshi;Kensuke Fukumitsu;Shimpei Masuda;Marwan Hamze;Tomoya Sasaki;Mitsuharu Morisawa;Eiichi Yoshida;
Pages: 7819 - 7826
Abstract: Manipulation with whole-body contact by humanoid robots offers distinct advantages, including enhanced stability and reduced load. On the other hand, we need to address challenges such as the increased computational cost of motion generation and the difficulty of measuring broad-area contact. We therefore have developed a humanoid control system that allows a humanoid robot equipped with tactile sensors on its upper body to learn a policy for whole-body manipulation through imitation learning based on human teleoperation data. This policy, named tactile-modality extended ACT (TACT), has a feature to take multiple sensor modalities as input, including joint position, vision, and tactile measurements. Furthermore, by integrating this policy with retargeting and locomotion control based on a biped model, we demonstrate that the life-size humanoid robot RHP7 Kaleido is capable of achieving whole-body contact manipulation while maintaining balance and walking. Through detailed experimental verification, we show that inputting both vision and tactile modalities into the policy contributes to improving the robustness of manipulation involving broad and delicate contact.
PubDate: MON, 16 JUN 2025 09:17:42 -04
Issue No: Vol. 10, No. 8 (2025)
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- Bio-Inspired Pneumatic Modular Actuator for Peristaltic Transport
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Authors: Brian Ye;Zhuonan Hao;Priya Shah;Mohammad Khalid Jawed;
Pages: 7827 - 7834
Abstract: Peristalsis, a biologically inspired mechanism, plays a crucial role in locomotion and material transport in living systems. While extensively studied in nature, its application in soft robotics for handling and transporting objects has seen progress but remains limited. This study presents a pneumatic modular actuator, fabricated from silicone polymer, that is scalable, adaptable, and repairable in situ. The system integrates donut-shaped actuation modules capable of radial and axial inflation, coupled with real-time pressure feedback for synchronized control across multiple stacked modules. Experimental validation demonstrates the actuator's ability to grasp and transport objects with diameters as small as 0.4 times its inner diameter at a speed of $2.08 \pm 0.07\ {\mathrm{mm/s}}$. The system successfully handles a range of object materials, including deformable soft tubes, solid handheld levels, and irregularly shaped bundles of pens. This work advances peristaltic actuation on object transportation, enabling safe and reliable manipulation of deformable and irregularly shaped materials across various applications, such as underwater specimen delivery and field robotics operations.
PubDate: THU, 12 JUN 2025 09:17:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- Minimally-Back-Drivable Robots for Rehabilitation: Path-Adherent
Permissiveness Control via Trajectory Adaptation-
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Authors: Erfan Shahriari;Johannes Lachner;Sami Haddadin;Neville Hogan;
Pages: 7835 - 7842
Abstract: In pursuit of effective robot-assisted rehabilitation, it is imperative that the robot facilitates rather than hinders the patient's self-movements in appropriate directions. This essential attribute, termed permissiveness, is often lacking in conventional industrial robots. In this letter, an innovative approach is introduced, enabling the robot to provide a controlled degree of permissiveness in specified directions. Central to the method is a novel mapping function that dynamically adjusts the desired trajectory along the therapeutic path in response to the patient's monitored physical effects. The proposed approach is agnostic to the type of controlled robot employed, rendering even minimally-back-drivable robots viable for rehabilitation purposes. The letter outlines the approach's diverse facets and potential applications, including an iterative phase-based support adaptation policy, and a series of experiments showcasing the practical feasibility.
PubDate: WED, 11 JUN 2025 09:17:15 -04
Issue No: Vol. 10, No. 8 (2025)
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- Reinforcement Learning for Multi-Agent Path Finding in Large-Scale
Warehouses via Distributed Policy Evolution-
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Authors: Qinru Shi;Meiqin Liu;Senlin Zhang;Xuguang Lan;
Pages: 7843 - 7850
Abstract: Efficient multi-agent path finding (MAPF) is essential for large-scale warehousing and logistics systems. Despite the potential of reinforcement learning (RL) methods, current approaches struggle with challenges such as inefficient exploration, poor generalization and inadequate deadlock resolution. To address these issues, we propose a novel evolutionary reinforcement learning (ERL) framework to address the MAPF problem in large-scale warehouse environments. Specifically, the framework leverages distributed policy evolution methods to provide diverse experiences, thereby improving policy training efficiency and policy performance. We further integrate curriculum learning into this framework to improve the generality of the policy and make it scalable to larger environments. Additionally, we introduce a deadlock-breaking mechanism based on expert experience, helping to mitigate deadlock issues in large-scale and high-density scenarios. Experiments show that our method outperforms existing methods across various environments, particularly excelling in complex scenarios with over 1,000 agents.
PubDate: THU, 12 JUN 2025 09:17:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- Swarm Navigation Based on Smoothed Particle Hydrodynamics in Complex
Obstacle Environments-
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Authors: Ruocheng Li;Bin Xin;Shuai Zhang;Mingzhe Lyu;Jinqiang Cui;
Pages: 7851 - 7858
Abstract: In this letter, we propose a method for the navigation of swarm uncrewed aerial vehicles (UAVs) in complex environments with obstacles. We propose an algorithmic framework based on Smoothed Particle Hydrodynamics (SPH). In this framework, each UAV is considered a particle, computing its motion information through local interactions with surrounding particles. Based on SPH, the UAV swarm can interactively adjust itself, allowing the entire cluster to advance in the flow pattern of an incompressible fluid. We introduce the Euclidean Signed Distance Field (ESDF) as a representation of the environment. The ESDF is constructed based on the obstacle information in the environment, enabling the swarm to deform and avoid obstacles within the environment. Simultaneously, we propose a swarm navigation function based on B-splines, rapidly obtaining executable trajectories by solving an unconstrained gradient optimization problem. Compared with existing methods, our algorithm exhibits significant improvements in success rate, stability, and scalability. Extensive simulations and physical experiments in both 2D and 3D environments have demonstrated the effectiveness of the proposed method.
PubDate: FRI, 13 JUN 2025 09:17:10 -04
Issue No: Vol. 10, No. 8 (2025)
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- Non-Conservative Efficient Collision Checking and Depth Noise-Awareness
for Trajectory Planning-
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Authors: Binh Nguyen;Manzur Murshed;Tanveer Choudhury;Kathleen Keogh;Gayan Kahandawa Appuhamillage;Linh Nguyen;
Pages: 7859 - 7866
Abstract: This letter presents MIDI (Minimum dIstance-based and Depth-noIse-aware), a novel trajectory planner that introduces non-conservative collision checking and depth noise awareness for robust autonomous navigation. Unlike existing collision-checking approaches that rely on trajectory discretization or geometric approximations of free space, MIDI evaluates each depth pixel independently against an entire trajectory at once. Thus, it bypasses both the notorious grid-size problem in trajectory discretization and conservativeness inherent in free space geometric approximations. Leveraging polynomial trajectory properties to compute minimum distances and collision probabilities for all obstacle points in closed-form, MIDI facilitates both non-conservative and real-time trajectory collision checking. Moreover, to the best of our knowledge, MIDI is the first memoryless planner that explicitly incorporates depth uncertainty information into online trajectory planning. Extensive simulations show that MIDI outperforms state-of-the-art memoryless planners, maintaining robust performance even under severe depth noise, where competing methods show significant degradation. The algorithm's non-conservative nature enables better utilization of free space, resulting in notably lower incompletion rates in cluttered environments. Finally, real-world flight trials were conducted to validate the effectiveness of our approach in an actual quadrotor.
PubDate: MON, 16 JUN 2025 09:17:43 -04
Issue No: Vol. 10, No. 8 (2025)
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- Zero-Shot Denoiser for Enhanced Acoustic Inspection: Mix Signal Separation
and Text-Guided Audio Reconstruction-
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Authors: Koki Shoda;Jun Younes Louhi Kasahara;Qi An;Atsushi Yamashita;
Pages: 7867 - 7874
Abstract: Acoustic inspection is crucial for infrastructure maintenance, but its effectiveness is often hampered by environmental noise. Conventional denoising methods rely on prior knowledge or training data, limiting their practicability. This letter presents Zero-Shot Denoiser, a novel approach achieving noise reduction without pre-collected target sound samples or noise knowledge. Our method synergistically combines Mix Signal Separation (MSS) for unsupervised audio decomposition and Artifact-Resilient Attention (AR-Attention) for text-guided audio reconstruction. AR-Attention leverages pre-trained audio-language models and dual normalization to mitigate BSS artifacts and identify target sounds semantically. We introduce pseudo Signal-to-Noise Ratio, derived from the audio-language model, for automatic BSS hyperparameter optimization. In experiments using public datasets, our method, operating in a true zero-shot setting, achieved performance comparable to that of state-of-the-art supervised denoising methods, and experiments targeting hammering tests confirmed the effectiveness of our approach for real-world acoustic inspections. Our approach overcomes the limitations of data-dependent techniques and offers a versatile noise reduction solution for acoustic inspection and broader acoustic tasks.
PubDate: MON, 16 JUN 2025 09:17:43 -04
Issue No: Vol. 10, No. 8 (2025)
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- VR-Robo: A Real-to-Sim-to-Real Framework for Visual Robot Navigation and
Locomotion-
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Authors: Shaoting Zhu;Linzhan Mou;Derun Li;Baijun Ye;Runhan Huang;Hang Zhao;
Pages: 7875 - 7882
Abstract: Recent success in legged robot locomotion is attributed to the integration of reinforcement learning and physical simulators. However, these policies often encounter challenges when deployed in real-world environments due to sim-to-real gaps, as simulators typically fail to replicate visual realism and complex real-world geometry. Moreover, the lack of realistic visual rendering limits the ability of these policies to support high-level tasks requiring RGB-based perception like ego-centric navigation. This letter presents a Real-to-Sim-to-Real framework that generates photorealistic and physically interactive “digital twin” simulation environments for visual navigation and locomotion learning. Our approach leverages 3D Gaussian Splatting (3DGS) based scene reconstruction from multi-view images and integrates these environments into simulations that support ego-centric visual perception and mesh-based physical interactions. To demonstrate its effectiveness, we train a reinforcement learning policy within the simulator to perform a visual goal-tracking task. Extensive experiments show that our framework achieves RGB-only sim-to-real policy transfer. Additionally, our framework facilitates the rapid adaptation of robot policies with effective exploration capability in complex new environments, highlighting its potential for applications in households and factories.
PubDate: MON, 02 JUN 2025 09:18:37 -04
Issue No: Vol. 10, No. 8 (2025)
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- VarWrist: An Anthropomorphic Soft Wrist With Variable Stiffness
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Authors: Chaozhou Zhang;Min Li;Zhanshuo Yang;Xiangrui Kong;Jiayi Luo;Yushen Liu;Jian Fu;Guanghua Xu;Shan Luo;
Pages: 7883 - 7890
Abstract: Robotic wrists play a crucial role in enhancing the dexterity and stability of robotic end-effectors. Existing rigid robotic wrists tend to be complex and lack flexibility, while soft robotic wrists often struggle with limited load-bearing capacity and lower accuracy. Human wrists feature multi-degrees of freedom and variable stiffness, which help human hands to accomplish daily tasks. This study presents an innovative anthropomorphic soft robotic wrist, VarWrist, equipped with a fiber jamming variable stiffness module, enabling stiffness adjustment through vacuuming. VarWrist consists of three parallel bellows, utilizing a positive-negative pneumatic actuation strategy to mimic human wrist motion. In addition, the trajectory equation of the rotation center was fitted through modeling. We developed a prototype of VarWrist and assessed its performance. Results indicate that the soft wrist surpasses the motion range of human wrists, achieving flexion (81.9°), extension (78.5°), ulnar deviation (70.5°), and radial deviation (70.5°). The bending motion trajectory showed a 73% increase in similarity to human motion compared to fixed-axis rotation, with VarWrist exhibiting a significant range of variable stiffness (resting state: 206%, working state: 155%). Demonstration experiments confirm that this wrist facilitates a dexterous hand in completing grasping tasks that would be unattainable by the hand alone.
PubDate: FRI, 13 JUN 2025 09:17:10 -04
Issue No: Vol. 10, No. 8 (2025)
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- Electronics-Free 3D-Printed Soft Swimming Robot With Pneumatic Oscillating
Control for Efficient Undulating Locomotion-
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Authors: Yichen Zhai;Michael T. Tolley;
Pages: 7891 - 7898
Abstract: Soft robots, with their compliance and adaptability, are ideal for applications requiring continuously flexible, dynamic movement, making them promising candidates for underwater locomotion. However, current swimming soft robots often rely on electronic power sources and complex, labor-intensive manufacturing, limiting their scalability and use in challenging environments. Recent advancements in 3D printing, particularly fused filament fabrication (FFF), offer a practical alternative for fabricating soft robots, enabling monolithic structures that require minimal assembly. In this work, we introduce a pneumatically powered, electronics-free swimming robot, fully fabricated from soft thermoplastic elastomer (TPE) using a desktop FFF 3D printer. Inspired by the morphology of the tadpole, our design incorporates a pneumatic oscillating controller as the “brain” and segmented actuators as the “tail,” enabling autonomous undulating propulsion without electronics. We demonstrate untethered operation using a portable CO2 canister and characterize two robot configurations optimized for efficient swimming. The robots achieve controlled oscillation and effective underwater movement, reaching a maximum speed of 0.70 body lengths per second (BL/s). This electronics-free, 3D-printed design represents a step forward in creating low-cost, accessible soft robotic platforms, suited for exploration in aquatic environments where electronics are impractical.
PubDate: THU, 12 JUN 2025 09:17:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- I2D-LocX: An Efficient, Precise and Robust Method for Camera Localization
in LiDAR Maps-
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Authors: Huai Yu;Xubo Zhu;Shu Han;Wen Yang;Gui-Song Xia;
Pages: 7899 - 7906
Abstract: Camera localization within LiDAR maps has gained significant attention due to its potential for accurate positioning with low-cost and lightweight sensors compared to LiDAR-based systems. However, existing methods often prioritize localization accuracy, sometimes compromising efficiency, which can limit their suitability for real-time applications. To address these issues, we propose I2D-LocX, a lightweight monocular camera localization framework with three branches, establishing pixel-level and feature-level constraints to enhance localization performance without increasing model complexity. Specifically, the main branch generates a flow map to represent pixel-point displacements. One auxiliary branch shares the same input as the main branch and employs an additional decoder to evaluate the confidence of the flow map. The other auxiliary branch leverages a zero-flow generated from the displacement-free input to guide feature matching, thereby enhancing localization robustness. Notably, both auxiliary branches share parameters with the main branch and are omitted during inference, ensuring computational efficiency. Extensive experiments on benchmark datasets, including KITTI-Odometry, Argoverse, Waymo, and nuScenes, show that I2D-LocX can achieve centimeter-level localization accuracy with about 37 ms inference time, greatly improving the localization performance for real-world applications.
PubDate: THU, 19 JUN 2025 09:16:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- Semantic Hierarchy-Guided Adversarial Attack for Autonomous Driving
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Authors: Gwangbin Kim;SeungJun Kim;
Pages: 7907 - 7914
Abstract: Autonomous vehicles employ semantic segmentation as a foundational component for perception and scene understanding, upon which driving decisions can be informed. Despite their performance, these deep learning models remain susceptible to subtle input perturbations that can cause severe deviation in model output. To enhance algorithmic robustness by examining such vulnerabilities, researchers have investigated adversarial examples, which are visually imperceptible yet can severely degrade model performance. However, traditional attacks produce arbitrary misclassifications that ignore semantic relationships, making the attack less effective. This letter introduces a semantic hierarchy-guided adversarial attack (SHAA), a white-box adversarial attack against semantic segmentation for autonomous driving. By combining semantic hierarchy and adaptive momentum-based updates across the image, SHAA produces semantically nontrivial yet highly effective perturbations. The SHAA method exposes deeper vulnerabilities with a higher attack success rate in semantic segmentation than existing methods, aiding the design of a more resilient perception system for autonomous vehicles.
PubDate: WED, 18 JUN 2025 09:17:30 -04
Issue No: Vol. 10, No. 8 (2025)
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- OmniNet: Omnidirectional Jumping Neural Network With Height-Awareness for
Quadrupedal Robots-
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Authors: Yimin Han;Jiahui Zhang;Zeren Luo;Yingzhao Dong;Jinghan Lin;Liu Zhao;Shihao Dong;Peng Lu;
Pages: 7915 - 7922
Abstract: In the robotics community, it has been a longstanding challenge for quadrupeds to achieve highly explosive movements similar to their biological counterparts. In this work, we introduce a novel training framework that achieves height-aware and omnidirectional jumping for quadrupedal robots. To facilitate the precise tracking of the user-specified jumping height, our pipeline concurrently trains an estimator that infers the robot and its end-effector states in an online fashion. Besides, a novel reward is involved by solving the analytical inverse kinematics with pre-defined end-effector positions. Guided by this term, the robot is empowered to regulate its gestures during the aerial phase. In the comparative studies, we verify that this controller can not only achieve the longest relative forward jump distance, but also exhibit the most comprehensive jumping capabilities among all the existing jumping controllers. A video summarizing the methodology and the validation in both simulation and real hardware is submitted along with this paper.
PubDate: THU, 19 JUN 2025 09:16:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- A Modular Magnetic Navigation System for Actuating Surface Microwalkers
-
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Authors: Han Zhao;Qi Zhang;Jianing Li;Zhenlin Chen;Chongyun Wang;Zhen Liu;Dong Sun;
Pages: 7923 - 7930
Abstract: The complex motion modes of surface microwalkers rely on magnetic torque generated by rotating/oscillating magnetic fields. Actuation systems based on rotating permanent magnets exhibit considerable advantages in generating these dynamic fields due to their high flexibility. However, current omnidirectional magnet control systems are complex in design, while single-axis rotational control systems are limited to specific motion modes. This study presents a modular magnetic navigation system based on the quasi-omnidirectional magnet control, aiming to balance control complexity and system functionality. This system utilizes a dual-sided layout, with the rotating magnet system and imaging system positioned on opposite sides of the sample module, thereby reducing control complexity for target tracking in a dynamic field of view. The dual-axis rotation control of the magnet is achieved by a decoupled two-degree-of-freedom rotating platform, enabling the switching of multiple rotation fields. Various rotating magnetic fields generated by the proposed system are modeled, and different motion modes of surface microwalkers driven by the system are analyzed. Based on this, corresponding motion control strategies are provided for different motion modes. Finally, experiments are performed to verify the performance of the proposed system in actuating both rolling and swarm motion of surface microwalkers. The results demonstrate that the proposed modular navigation system has comparable actuation capabilities to existing systems, but with lower control complexity, suggesting its potential for designing magnetic actuation systems in clinical applications.
PubDate: THU, 08 MAY 2025 09:17:27 -04
Issue No: Vol. 10, No. 8 (2025)
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- FAST-LIVO2 on Resource-Constrained Platforms: LiDAR-Inertial-Visual
Odometry With Efficient Memory and Computation-
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Authors: Bingyang Zhou;Chunran Zheng;Ziming Wang;Fangcheng Zhu;Yixi Cai;Fu Zhang;
Pages: 7931 - 7938
Abstract: This paper presents a lightweight LiDAR-inertial-visual odometry system optimized for resource-constrained platforms. It integrates a degeneration-aware adaptive visual frame selector into error-state iterated Kalman filter (ESIKF) with sequential updates, improving computation efficiency markedly while maintaining a similar level of robustness. Additionally, a memory-efficient mapping structure combining a locally unified visual-LiDAR map and a long-term visual map achieves a good trade-off between performance and memory usage. Extensive experiments on x86 and ARM platforms demonstrate the system's robustness and efficiency. On the Hilti dataset, our system achieves a 33% reduction in per-frame runtime and 47% lower memory usage compared to FAST-LIVO2, with only a 3 cm increase in RMSE. Despite this slight accuracy trade-off, our system remains competitive, outperforming state-of-the-art (SOTA) LIO methods such as FAST-LIO2 and most existing LIVO systems. These results validate the system's capability for scalable deployment on resource-constrained edge computing platforms.
PubDate: THU, 19 JUN 2025 09:16:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- ToMPC: Task-Oriented Model Predictive Control via ADMM for Safe Robotic
Manipulation-
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Authors: Xinyu Jia;Wenxin Wang;Jun Yang;Yongping Pan;Haoyong Yu;
Pages: 7939 - 7946
Abstract: This letter proposes a task-oriented model predictive control (ToMPC) framework for safe and efficient robotic manipulation in open workspaces. The framework unifies collision-free motion and robot-environment interaction to address diverse scenarios. Additionally, it introduces task-oriented obstacle avoidance that leverages kinematic redundancy to enhance manipulation efficiency in obstructed environments. This complex optimization problem is solved by the alternating direction method of multipliers (ADMM), which decomposes the problem into two subproblems tackled by differential dynamic programming (DDP) and quadratic programming (QP), respectively. The effectiveness of this approach is validated in simulation and hardware experiments on a Franka Panda robotic manipulator. Results demonstrate that the framework can plan motion and/or force trajectories in real time, maximize the manipulation range while avoiding obstacles, and strictly adhere to safety-related hard constraints.
PubDate: THU, 12 JUN 2025 09:17:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- Tightly-Coupled LiDAR-IMU-Leg Odometry With Online Learned Leg Kinematics
Incorporating Foot Tactile Information-
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Authors: Taku Okawara;Kenji Koide;Aoki Takanose;Shuji Oishi;Masashi Yokozuka;Kentaro Uno;Kazuya Yoshida;
Pages: 7947 - 7954
Abstract: In this letter, we present tightly coupled LiDAR-IMU-leg odometry, which is robust to challenging conditions such as featureless environments and deformable terrains. We developed an online learning-based leg kinematics model named the neural leg kinematics model, which incorporates tactile information (foot reaction force) to implicitly express the nonlinear dynamics between robot feet and the ground. Online training of this model enhances its adaptability to weight load changes of a robot (e.g., assuming delivery or transportation tasks) and terrain conditions. According to the neural adaptive leg odometry factor and online uncertainty estimation of the leg kinematics model-based motion predictions, we jointly solve online training of this kinematics model and odometry estimation on a unified factor graph to retain the consistency of both. The proposed method was verified through real experiments using a quadruped robot in two challenging situations: 1) a sandy beach, representing an extremely featureless area with a deformable terrain, and 2) a campus, including multiple featureless areas and terrain types of asphalt, gravel (deformable terrain), and grass. Experimental results showed that our odometry estimation incorporating the neural leg kinematics model outperforms state-of-the-art works.
PubDate: MON, 16 JUN 2025 09:17:43 -04
Issue No: Vol. 10, No. 8 (2025)
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- Robust State Estimation for Legged Robots With Dual Beta Kalman Filter
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Authors: Tianyi Zhang;Wenhan Cao;Chang Liu;Tao Zhang;Jiangtao Li;Shengbo Eben Li;
Pages: 7955 - 7962
Abstract: Existing state estimation algorithms for legged robots that rely on proprioceptive sensors often overlook foot slippage and leg deformation in the physical world, leading to large estimation errors. To address this limitation, we propose a comprehensive measurement model that accounts for both foot slippage and variable leg length by analyzing the relative motion between foot contact points and the robot's body center. We show that leg length is an observable quantity, meaning that its value can be explicitly inferred by designing an auxiliary filter. To this end, we introduce a dual estimation framework that iteratively employs a parameter filter to estimate the leg length parameters and a state filter to estimate the robot's state. To prevent error accumulation in this iterative framework, we construct a partial measurement model for the parameter filter using the leg static equation. This approach ensures that leg length estimation relies solely on joint torques and foot contact forces, avoiding the influence of state estimation errors on the parameter estimation. Unlike leg length that can be directly estimated, foot slippage cannot be measured directly with the current sensor configuration. However, since foot slippage occurs at a low frequency, it can be treated as outliers in the measurement data. To mitigate the impact of these outliers, we propose the $\beta$-Kalman filter ($\beta$-KF), which redefines the estimation loss in canonical Kalman filtering using $\beta$-divergence. This divergence can assign low weights to outliers in an adaptive manner, thereby enhancing the robustness of the estimation algorithm. These techniques together form the dual $\beta$-Kalman filter (Dual $\beta$-KF), a novel algorithm for robust state estimation in legged robots. Experimental results on the Unitree GO2 robot demonstrate that the Dual $\beta$-KF significantly outperforms state-of-the-art methods.
PubDate: FRI, 13 JUN 2025 09:17:10 -04
Issue No: Vol. 10, No. 8 (2025)
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- Harnessing the Power of Vibration Motors to Develop Miniature Untethered
Robotic Fishes-
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Authors: Chongjie Jiang;Yingying Dai;Jinyang Le;Xiaomeng Chen;Yu Xie;Wei Zhou;Fuzhou Niu;Ying Li;Tao Luo;
Pages: 7963 - 7970
Abstract: Miniature underwater robots play a crucial role in the exploration and development of marine resources, particularly in confined spaces and high-pressure deep-sea environments. This study presents the design, optimization, and performance of a miniature robotic fish, powered by the oscillation of bio-inspired fins. These fins feature a rigid-flexible hybrid structure and use an eccentric rotating mass (ERM) vibration motor as the excitation source to generate high-frequency unidirectional oscillations that induce acoustic streaming for propulsion. The drive mechanism, powered by miniature ERM vibration motors, eliminates the need for complex mechanical drive systems, enabling complete isolation of the entire drive system from the external environment and facilitating the miniaturization of the robotic fish. A compact, untethered robotic fish, measuring 85 × 60 × 45 mm3, is equipped with three bio-inspired fins located at the pectoral and caudal positions. Experimental results demonstrate that the robotic fish achieves a maximum forward swimming speed of 1.36 body lengths (BL) per second powered by all fins and minimum turning radius of 0.6 BL when powered by a single fin. In addition, the robotic fish is able to swim upstream in turbulent flow, and its autonomous version can navigate complex, obstacle-filled environments. These results underscore the significance of employing the ERM vibration motor in advancing the development of highly maneuverable, miniature untethered underwater robots for various marine exploration tasks.
PubDate: THU, 19 JUN 2025 09:16:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- Tailless Flapping-Wing Robot With Bio-Inspired Elastic Passive Legs for
Multi-Modal Locomotion-
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Authors: Zhi Zheng;Xiangyu Xu;Jin Wang;Yikai Chen;Jingyang Huang;Ruixin Wu;Huan Yu;Guodong Lu;
Pages: 7971 - 7978
Abstract: Flapping-wing robots offer significant versatility; however, achieving efficient multi-modal locomotion remains challenging. This letter presents the design, modeling, and experimentation of a novel tailless flapping-wing robot with three independently actuated pairs of wings. Inspired by the leg morphology of juvenile water striders, the robot incorporates bio-inspired elastic passive legs that convert flapping-induced vibrations into directional ground movement, enabling locomotion without additional actuators. This vibration-driven mechanism facilitates lightweight, mechanically simplified multi-modal mobility. An SE(3)-based controller coordinates flight and mode transitions with minimal actuation. To validate the robot's feasibility, a functional prototype was developed, and experiments were conducted to evaluate its flight, ground locomotion, and mode-switching capabilities. Results show satisfactory performance under constrained actuation, highlighting the potential of multi-modal flapping-wing designs for future aerial-ground robotic applications. These findings provide a foundation for future studies on frequency-based terrestrial control and passive yaw stabilization in hybrid locomotion systems.
PubDate: MON, 16 JUN 2025 09:17:43 -04
Issue No: Vol. 10, No. 8 (2025)
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- Estimation of Slip Ratio and Side Slip Angle of Wheeled Planetary Rovers
Based on Trace Imprint-
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Authors: Nan Li;Junlong Guo;Liang Ding;Chenghua Tian;Chuan Zhou;Haibo Gao;
Pages: 7979 - 7986
Abstract: This letter proposes a method to estimate the wheel slip ratio and side slip angle of wheeled rovers by processing images of wheel trace imprints. The proposed method extracts structural features from trace imprint images, such as the trace unit, trace contour, and angle between the centerline of the trace unit and contour. The relationships between the structural trace imprint features and the wheel slip ratio and side slip angle have been revealed after a study of the underlying mechanism of trace imprint formation, with consideration of the kinematics of the wheel lug and lug-soil interaction. These relationships are then used to estimate wheel slip ratio and side slip angle. Compared with the existing estimation methods, the proposed method can estimate longitudinal slippage and lateral drift simultaneously that typically occur in planetary rovers during traverse of cross slopes. The effectiveness of the proposed method has been demonstrated by experiments using a rover wheel test-bed under various conditions.
PubDate: THU, 19 JUN 2025 09:16:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- Fluoroscopic Shape and Pose Tracking of Catheters With Custom Radiopaque
Markers-
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Authors: Jared Lawson;Rohan Chitale;Nabil Simaan;
Pages: 7987 - 7994
Abstract: Safe navigation of steerable and robotic catheters in the cerebral vasculature requires awareness of the catheter's shape and pose. Currently, a significant perception burden is placed on interventionalists to mentally reconstruct and predict catheter motions from biplane fluoroscopy images. Efforts to track these catheters are limited to planar segmentation or bulky sensing instrumentation, which are incompatible with microcatheters used in neurointervention. In this work, a catheter is equipped with custom radiopaque markers arranged to enable simultaneous shape and pose estimation under biplane fluoroscopy. A design measure is proposed to guide the arrangement of these markers to minimize sensitivity to marker tracking uncertainty. This approach was deployed for microcatheters ($\leq \phi {\text{2}}\,\text {mm}$) navigating phantom vasculature with shape tracking errors
PubDate: THU, 19 JUN 2025 09:16:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- SPLASH-SegFormer Pipeline: A Transformer-Based Approach for
High-Resolution and Low-Cost Laser Scanner Seafloor Mapping-
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Authors: Javiera Fuentes-Guíñez;Giancarlo Troni;Hans Lobel;
Pages: 7995 - 8002
Abstract: High-resolution seafloor mapping continues to be challenging, primarily due to the high costs and complexity of traditional sensors. Laser scanners offer a more affordable alternative, using a monocular camera and a laser stripe. While this method provides high-resolution 3D reconstructions, it is sensitive to underwater lighting and surface reflections. This work introduces SPLASH-SegFormer Pipeline, a novel method that leverages attention mechanisms and multi-level feature integration to enhance laser stripe segmentation in underwater environments, marking, to the best of our knowledge, the first application of a transformer-based architecture for this task. The proposed method is trained and evaluated using both simulated and field experimental data collected in a test tank and during multiple sea expeditions, allowing it to learn from diverse and challenging environments. A complete pipeline is constructed around this network, enabling the processing of images from a camera to a 3D point cloud reconstruction quickly and accurately. The results demonstrate that SPLASH-SegFormer significantly enhances segmentation robustness and accuracy while maintaining a reasonable speed. It outperforms traditional underwater thresholding by over 64 points in mIoU and surpasses all evaluated methods, achieving 6.6 points higher mIoU than Stripe Segmentor while running over 65% faster. This speed enhances efficiency and makes it ideal for real-time onboard applications, enabling millimeter-level underwater 3D reconstruction and high-resolution seafloor mapping using smaller, cost-effective sensors.
PubDate: FRI, 06 JUN 2025 09:17:27 -04
Issue No: Vol. 10, No. 8 (2025)
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- LoopRefine: Deep Camera Pose Estimation With Loop Consistency
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Authors: Zhiwei Wang;Hui Deng;Jiawei Shi;Mochu Xiang;Zhicheng Lu;Qi Liu;Yuchao Dai;
Pages: 8003 - 8010
Abstract: Recently, pose estimation under sparse views ($\leq 10$) has witnessed significant advances with the development of deep learning. Most existing methods directly regress the absolute poses, demonstrating leading performance on benchmarks. However, directly regressing the scaled poses using deep neural networks is inherently ill-posed, resulting in overfitted models that perform poorly on diverse scenarios. In contrast, we resort to the well-posed solutions from traditional Structure-from-Motion (SfM) pipelines and propose LoopRefine, a diffusion model that assumes known camera intrinsics and estimates pairwise normalized camera relative poses and utilizes triplet coplanar constraints to align the scale of camera poses. Like traditional SfM methods, LoopRefine incrementally constructs camera triplets, and the scale ambiguities are resolved by gradually recovering the scale of poses and connecting the pose graph. To further improve the pose estimation accuracy during inference, we explore pose compatibility by randomly chaining the loop transformations on the pose graph and organizing iterative loop consistency-based optimization. Extensive experiments demonstrate the superiority of our method, and the generalization performance on both object-centered datasets and scene datasets also proves the effectiveness of integrated geometric constraints.
PubDate: THU, 19 JUN 2025 09:16:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- Bioinspired Microrobot Climbing on Fabrics Using a Single Actuator
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Authors: Jiliang Ma;Jun Peng;Yusen Ma;Kanglong Yuan;Ao Qin;Wenwu Zhu;Xuefeng Chen;
Pages: 8011 - 8018
Abstract: Microrobots climbing on fabrics are well-suited for reconnaissance and rescue tasks in indoor environments. However, achieving stable adhesion while maintaining a simplified locomotion mechanism remains a formidable challenge. This study presents a 4 cm, 18.2 g climbing robot designed with a single-actuator, enabling dual-degree-of-freedom motion on fabric surfaces through the synergistic integration of anisotropic microstructures. The robot's compact form achieves complex functionalities, incorporating locomotion, imaging, illumination, and real-time video transmission capabilities. Innovative hook-shaped microstructures overcome challenges in gripping rough and fibrous surfaces (wood, burlap, mesh, etc.), allowing robots to climb on slopes up to 55° and maintain stillness on inverted surfaces. This work demonstrates the robot's feasibility and efficiency for reconnaissance applications on varied indoor surfaces through structural design optimization and performance testing in simulated real-world environments.
PubDate: THU, 19 JUN 2025 09:16:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- Tightly Coupled SLAM With Imprecise Architectural Plans
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Authors: Muhammad Shaheer;Jose Andres Millan-Romera;Hriday Bavle;Marco Giberna;Jose Luis Sanchez-Lopez;Javier Civera;Holger Voos;
Pages: 8019 - 8026
Abstract: Robots navigating indoor environments often have access to architectural plans, which can serve as prior knowledge to enhance their localization and mapping capabilities. While some SLAM algorithms leverage these plans for global localization in real-world environments, they typically overlook a critical challenge: the “as-planned” architectural designs frequently deviate from the “as-built” real-world environments. To address this gap, we present a novel algorithm that tightly couples LIDAR-based simultaneous localization and mapping with architectural plans in the presence of deviations. Our method utilizes a multi-layered semantic representation to not only localize the robot, but also to estimate global alignment and structural deviations between “as-planned” and “as-built” environments in real-time. To validate our approach, we performed experiments in simulated and real datasets demonstrating robustness to structural deviations up to 35 cm and $15^\circ$. On average, our method achieves 43% less localization error than baselines in simulated environments, while in real environments, the “as-built” 3D maps show 7% lower average alignment error.
PubDate: MON, 23 JUN 2025 09:17:22 -04
Issue No: Vol. 10, No. 8 (2025)
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- Bimanual Regrasp Planning and Control for Active Reduction of Object Pose
Uncertainty-
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Authors: Ryuta Nagahama;Weiwei Wan;Zhengtao Hu;Kensuke Harada;
Pages: 8027 - 8034
Abstract: Precisely grasping an object is a challenging task due to pose uncertainties. Conventional methods have used cameras and fixtures to reduce object uncertainty. They are effective but require intensive preparation, such as designing jigs based on the object geometry and calibrating cameras with high-precision tools fabricated using lasers. In this study, we propose a method to reduce the uncertainty of the position and orientation of a grasped object without using a fixture or a camera. Our method is based on the concept that the flat finger pads of a parallel gripper can reduce uncertainty along its opening/closing direction through flat surface contact. Three approximately orthogonal grasps by parallel grippers with flat finger pads collectively constrain an object's position and orientation to a unique state. Guided by the concepts, we develop a regrasp planning and admittance control approach that sequentially finds and leverages three approximately orthogonal grasps of two robotic arms to actively reduce uncertainties in the object pose. We evaluated the proposed method on different initial object uncertainties and verified that it had good repeatability. The deviation levels of the experimental trials were on the same order of magnitude as those of an optical tracking system, demonstrating strong relative inference performance.
PubDate: MON, 16 JUN 2025 09:17:43 -04
Issue No: Vol. 10, No. 8 (2025)
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- Degradation-Aware LiDAR-Thermal-Inertial SLAM
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Authors: Yu Wang;Yufeng Liu;Lingxu Chen;Haoyao Chen;Shiwu Zhang;
Pages: 8035 - 8042
Abstract: During robotic disaster relief missions, state estimation still faces significant challenges, especially when GNSS is denied or sensor perception undergoes degradation. In this letter, we introduce a degradation-aware LiDAR-Thermal-Inertial SLAM, DaLiTI, that leverages the complementary nature of multi-modal information to achieve robust and precise state estimation in perceptually challenging environments. The system utilizes an iterated error state Kalman filter (IESKF) to loosely integrate LiDAR, thermal infrared camera, and IMU measurements. We propose an adaptive fusion mechanism that dynamically weights and fuses LiDAR and thermal measurements based on real-time modal quality to prevent failure information from propagating throughout the system. Experimental results demonstrate that, compared with state-of-the-art methods, DaLiTI maintains competitive performance in conventional environments and exhibits superior robustness and accuracy in degraded scenarios such as fire scenes or chemical plants with gas leaks.
PubDate: THU, 19 JUN 2025 09:16:58 -04
Issue No: Vol. 10, No. 8 (2025)
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- Future-Oriented Navigation: Dynamic Obstacle Avoidance With One-Shot
Energy-Based Multimodal Motion Prediction-
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Authors: Ze Zhang;Georg Hess;Junjie Hu;Emmanuel Dean;Lennart Svensson;Knut Åkesson;
Pages: 8043 - 8050
Abstract: This letter proposes an integrated approach for the safe and efficient control of mobile robots in dynamic and uncertain environments. The approach consists of two key steps: one-shot multimodal motion prediction to anticipate motions of dynamic obstacles and model predictive control to incorporate these predictions into the motion planning process. Motion prediction is driven by an energy-based neural network that generates high-resolution, multi-step predictions in a single operation. The prediction outcomes are further utilized to create geometric shapes formulated as mathematical constraints. Instead of treating each dynamic obstacle individually, predicted obstacles are grouped by proximity in an unsupervised way to improve performance and efficiency. The overall collision-free navigation is handled by model predictive control with a specific design for proactive dynamic obstacle avoidance. The proposed approach allows mobile robots to navigate effectively in dynamic environments. Its performance is accessed across various scenarios that represent typical warehouse settings. The results demonstrate that the proposed approach outperforms other existing dynamic obstacle avoidance methods.
PubDate: MON, 02 JUN 2025 09:18:37 -04
Issue No: Vol. 10, No. 8 (2025)
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- Multi-Strategy Enhanced Particle Swarm Optimization for Variable Curvature
Path Planning in Flexible Needle Insertion-
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Authors: Yanding Qin;Jianing Teng;Chao Wen;Ge Fang;Hongpeng Wang;Jianda Han;
Pages: 8051 - 8058
Abstract: Flexible needles provide enhanced adaptability for navigating puncture pathways and avoiding obstacles when compared to conventional rigid needles. However, developing a three dimensional (3D) curved path for flexible needle is challenging, particularly in achieving both effective obstacle avoidance and precise targeting. To this end, we proposed an improved particle swarm optimization-based path planning approach by incorporating good point set initialization and heuristic multi-mutation strategy. Such incorporation greatly enhanced the algorithm's global search capability while ensuring fast convergence speed. In addition, 3D biarc curve fitting was employed to develop a kinematically reachable path for bevel tip needles. Obstacle-avoidance simulations conducted demonstrate the superior performance of proposed method against state-of-the-art algorithms in the aspect of path length and distance to obstacles, repeatability and local minima trap avoidance. Needle puncturing experiments performed using duty cycling control achieved a small curvature radius of 49.6 mm and targeting errors of less than 4 mm. This algorithm facilitates efficient variable curvature path planning for flexible needles, ensuring precise targeting while effectively avoiding obstacles.
PubDate: MON, 16 JUN 2025 09:17:43 -04
Issue No: Vol. 10, No. 8 (2025)
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- Optimization of Preemptive Impact Mitigation Without Prior Collision
Testing-
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Authors: Hayato Nakamura;Hikaru Arita;Shunsuke Tokiwa;Kenji Tahara;
Pages: 8059 - 8066
Abstract: Effective impact mitigation strategies are crucial for preventing potential damage to both robotic systems and their operational environments during high-velocity and dynamic maneuvers, as well as during the execution of high-precision tasks. The successful implementation of impact mitigation strategies in real-world applications fundamentally requires appropriate parameter tuning. However, owing to the destructive nature of collisions, heuristic parameter tuning is impractical, as it risks damage to both the robotic system and its operational environment during experimental trials. This study eliminates the need for preliminary collision experiments in parameter optimization by introducing a novel methodology that leverages recent proximity sensor-based preemptive impact mitigation strategies that reframe impact mitigation as a geometric rather than physical problem. The key innovation of this work lies in the reformulation of the proximity sensor output to enable both the analytical derivation of preemptive motion trajectories and the direct application of standard optimization solvers. The effectiveness of the proposed methodology is validated through numerical simulations and two different experimental configurations. By eliminating the need for collision trials, robotic systems can safely execute potentially destructive tasks that would otherwise result in system damage without proper impact mitigation.
PubDate: FRI, 20 JUN 2025 09:17:10 -04
Issue No: Vol. 10, No. 8 (2025)
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