Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Andres Galeote-Luque;Jose-Raul Ruiz-Sarmiento;Javier Gonzalez-Jimenez;
Pages: 6923 - 6930 Abstract: We present a new fast Ground Decoupled 3D Lidar Odometry (GND-LO) method. The particularity of GND-LO is that it takes advantage of the distinct spatial layout found in urban settings to efficiently recover the lidar movement in a decoupled manner. For that, the input scans are reduced to a set of planar patches extracted from the flat surfaces of the scene, found aplenty in these scenarios. These patches can be labeled as either belonging to the ground or walls, decoupling the estimation into two steps. First, the ground planes from each scan, clustered from the ground patches, are registered. Then, the motion estimation is completed by minimizing the distance between the wall patches and their corresponding points from the other scan, whose pairing is iteratively updated. GND-LO has demonstrated to perform both precisely and efficiently beating state-of-the-art approaches. Concretely, experiments on the popular KITTI dataset show that our proposal outperforms its competitors by reducing the average drift by 19% in translation and 4% in rotation. This is achieved by running in real-time without needing GPU or optimized multi-threading, as it is commonplace in the literature. PubDate:
MON, 18 SEP 2023 14:11:09 -04 Issue No:Vol. 8, No. 11 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Jan Ole von Hartz;Eugenio Chisari;Tim Welschehold;Wolfram Burgard;Joschka Boedecker;Abhinav Valada;
Pages: 6931 - 6938 Abstract: In policy learning for robotic manipulation, sample efficiency is of paramount importance. Thus, learning and extracting more compact representations from camera observations is a promising avenue. However, current methods often assume full observability of the scene and struggle with scale invariance. In many tasks and settings, this assumption does not hold as objects in the scene are often occluded or lie outside the field of view of the camera, rendering the camera observation ambiguous with regard to their location. To tackle this problem, we present BASK, a Bayesian approach to tracking scale-invariant keypoints over time. Our approach successfully resolves inherent ambiguities in images, enabling keypoint tracking on symmetrical objects and occluded and out-of-view objects. We employ our method to learn challenging multi-object robot manipulation tasks from wrist camera observations and demonstrate superior utility for policy learning compared to other representation learning techniques. Furthermore, we show outstanding robustness towards disturbances such as clutter, occlusions, and noisy depth measurements, as well as generalization to unseen objects both in simulation and real-world robotic experiments. PubDate:
MON, 18 SEP 2023 14:11:09 -04 Issue No:Vol. 8, No. 11 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Giorgio Valsecchi;Nikita Rudin;Lennart Nachtigall;Konrad Mayer;Fabian Tischhauser;Marco Hutter;
Pages: 6939 - 6946 Abstract: This letter introduces Barry, a dynamically balancing quadruped robot optimized for high payload capabilities and efficiency. It presents a new high-torque and low-inertia leg design, which includes custom-built high-efficiency actuators and transparent, sensorless transmissions. The robot's reinforcement learning-based controller is trained to fully leverage the new hardware capabilities to balance and steer the robot. The newly developed controller can manage the non-linearities introduced by the new leg design and handle unmodeled payloads up to 90 kg while operating at high efficiency. The approach's efficacy is demonstrated by a high payload-to-weight ratio verified with multiple tests, with a maximum ratio of 2 on flat terrain. Experiments also demonstrate Barry's power consumption and cost of transport, which converge to a value of 0.7 at 1.4 m/s, regardless of the payload mass. PubDate:
MON, 18 SEP 2023 14:11:09 -04 Issue No:Vol. 8, No. 11 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Muhammad Suhail Saleem;Rishi Veerapaneni;Maxim Likhachev;
Pages: 6947 - 6954 Abstract: In manipulation tasks like plug insertion or assembly that have low tolerance to errors in pose estimation (errors of the order of 2 mm can cause task failure), the utilization of touch/contact modality can aid in accurately localizing the object of interest. Motivated by this, in this work we model high-precision insertion tasks as planning problems under pose uncertainty, where we effectively utilize the occurrence of contacts (or the lack thereof) as observations to reduce uncertainty and reliably complete the task. We present a preprocessing-based planning framework for high-precision insertion in repetitive and time-critical settings, where the set of initial pose distributions (identified by a perception system) is finite. The finite set allows us to enumerate the possible planning problems that can be encountered online and preprocess a database of policies. Due to the computational complexity of constructing this database, we propose a general experience-based POMDP solver, E-RTDP-Bel, that uses the solutions of similar planning problems as experience to speed up planning queries and use it to efficiently construct the database. We show that the developed algorithm speeds up database creation by over a factor of 100, making the process computationally tractable. We demonstrate the effectiveness of the proposed framework in a real-world plug insertion task in the presence of port position uncertainty and a pipe assembly task in simulation in the presence of pipe pose uncertainty. PubDate:
MON, 18 SEP 2023 14:11:09 -04 Issue No:Vol. 8, No. 11 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Guanglin Cao;Mingcong Chen;Jian Hu;Hongbin Liu;
Pages: 6955 - 6962 Abstract: Intraoperative contact sensing has the potential to reduce the risk of surgical errors and enhance manipulation capabilities for medical robots, particularly in contact force control. Current intrinsic force sensing (IFS) methods are limited in application to medical instruments with arbitrary shape, due to high computational time and reliance on surface equations. This study presents an ultra-fast IFS method that uses multiple planes to establish surface geometry descriptions. The method can reduce high-order contact mechanical models that need to be solved iteratively to a set of linear equations, and calculate contact location analytically. In addition, a robot motion control approach based on the contact sensing method is proposed to maintain stable contact force and regulate the probe's orientation for robotic ultrasound systems (RUSS). Experimental results show that the contact sensing method is robust to friction and can achieve a mean ($pm$SD) displacement error of 1.04 $pm$ 0.43 mm in contact location with computational time less than 1 ms. The system has been evaluated on a phantom with sinusoidal motion. To the best of our knowledge, this is the first study to validate adaptiveness of RUSS under dynamic conditions. The results demonstrated that the system exhibits comparable manipulation capabilities to human operators with only force sensing, indicating a high level of adaptiveness. PubDate:
MON, 18 SEP 2023 14:11:09 -04 Issue No:Vol. 8, No. 11 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Hongyu Li;Snehal Dikhale;Soshi Iba;Nawid Jamali;
Pages: 6963 - 6970 Abstract: In this letter, we introduce ViHOPE, a novel framework for estimating the 6D pose of an in-hand object using visuotactile perception. Our key insight is that the accuracy of the 6D object pose estimate can be improved by explicitly completing the shape of the object. To this end, we introduce a novel visuotactile shape completion module that uses a conditional Generative Adversarial Network to complete the shape of an in-hand object based on volumetric representation. This approach improves over prior works that directly regress visuotactile observations to a 6D pose. By explicitly completing the shape of the in-hand object and jointly optimizing the shape completion and pose estimation tasks, we improve the accuracy of the 6D object pose estimate. We train and test our model on a synthetic dataset and compare it with the state-of-the-art. In the visuotactile shape completion task, we outperform the state-of-the-art by 265% using the Intersection of Union metric and achieve 88% lower Chamfer Distance. In the visuotactile pose estimation task, we present results that suggest our framework reduces position and angular errors by 35% and 64%, respectively. Furthermore, we ablate our framework to confirm the gain on the 6D object pose estimate from explicitly completing the shape. Ultimately, we show that our framework produces models that are robust to sim-to-real transfer on a real-world robot platform. PubDate:
MON, 18 SEP 2023 14:11:09 -04 Issue No:Vol. 8, No. 11 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Asia La Rocca;Matteo Saveriano;Andrea Del Prete;
Pages: 6971 - 6978 Abstract: Safety is often the most important requirement in robotics applications. Nonetheless, control techniques that can provide safety guarantees are still extremely rare for nonlinear systems, such as robot manipulators. A well-known tool to ensure safety is the viability kernel, which is the largest set of states from which safety can be ensured. Unfortunately, computing such a set for a nonlinear system is extremely challenging in general. Several numerical algorithms for approximating it have been proposed in the literature, but they suffer from the curse of dimensionality. This letter presents a new approach for numerically approximating the viability kernel of robot manipulators. Our approach solves optimal control problems to compute states that are guaranteed to be on the boundary of the set. This allows us to learn directly the set boundary, therefore learning in a smaller dimensional space. Compared to the state of the art on systems up to dimension 6, our algorithm resulted to be more than 2 times as accurate for the same computation time, or 6 times as fast to reach the same accuracy. PubDate:
MON, 18 SEP 2023 14:11:09 -04 Issue No:Vol. 8, No. 11 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
DongHoon Baek;Yu-Chen Chang;Joao Ramos;
Pages: 6979 - 6986 Abstract: Teleoperation has emerged as an alternative solution to fully-autonomous systems for achieving human-level capabilities on humanoids. Specifically, teleoperation with whole-body control is a promising hands-free strategy to command humanoids but requires more physical and mental demand. To mitigate this limitation, researchers have proposed shared-control methods incorporating robot decision-making to aid humans on low-level tasks, further reducing operation effort. However, shared-control methods for wheeled humanoid telelocomotion on a whole-body level has yet to be explored. In this work, we explore how whole-body bilateral feedback with haptics affects the performance of different shared-control methods for obstacle avoidance in diverse environments. A time-derivative Sigmoid function (TDSF) is implemented to generate more intuitive haptic feedback from obstacles. Comprehensive human experiments were conducted and the results concluded that bilateral feedback enhances the whole-body telelocomotion performance in unfamiliar environments but could reduce performance in familiar environments. Conveying the robot's intention through haptics showed further improvements since the operator can utilize the feedback for reactive short-distance planning and visual feedback for long-distance planning. PubDate:
MON, 18 SEP 2023 14:11:09 -04 Issue No:Vol. 8, No. 11 (2023)
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