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Authors:Indrajeet Yadav, Michael Sebok, Herbert G. Tanner Pages: 66 - 82 Abstract: The International Journal of Robotics Research, Volume 42, Issue 3, Page 66-82, March 2023. The paper presents a receding horizon planning and control strategy for quadrotor-type micro aerial vehicle (mav)s to navigate reactively and intercept a moving target in a cluttered unknown and dynamic environment. Leveraging a lightweight short-range sensor that generates a point-cloud within a relatively narrow and short field of view (fov), and an ssd-MobileNet based Deep neural network running on board the mav, the proposed motion planning and control strategy produces safe and dynamically feasible mav trajectories within the sensor fov, which the vehicle uses to autonomously navigate, pursue, and intercept its moving target. This task is completed without reliance on a global planner or prior information about the environment or the moving target. The effectiveness of the reported planner is demonstrated numerically and experimentally in cluttered indoor and outdoor environments featuring maximum speeds of up to 4.5–5 m/s. Citation: The International Journal of Robotics Research PubDate: 2023-05-26T06:32:03Z DOI: 10.1177/02783649231169803 Issue No:Vol. 42, No. 3 (2023)
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Authors:Clémentin Boittiaux, Claire Dune, Maxime Ferrera, Aurélien Arnaubec, Ricard Marxer, Marjolaine Matabos, Loïc Van Audenhaege, Vincent Hugel Abstract: The International Journal of Robotics Research, Ahead of Print. Visual localization plays an important role in the positioning and navigation of robotics systems within previously visited environments. When visits occur over long periods of time, changes in the environment related to seasons or day-night cycles present a major challenge. Under water, the sources of variability are due to other factors such as water conditions or growth of marine organisms. Yet, it remains a major obstacle and a much less studied one, partly due to the lack of data. This paper presents a new deep-sea dataset to benchmark underwater long-term visual localization. The dataset is composed of images from four visits to the same hydrothermal vent edifice over the course of 5 years. Camera poses and a common geometry of the scene were estimated using navigation data and Structure-from-Motion. This serves as a reference when evaluating visual localization techniques. An analysis of the data provides insights about the major changes observed throughout the years. Furthermore, several well-established visual localization methods are evaluated on the dataset, showing there is still room for improvement in underwater long-term visual localization. The data is made publicly available at seanoe.org/data/00810/92226/. Citation: The International Journal of Robotics Research PubDate: 2023-05-21T09:27:48Z DOI: 10.1177/02783649231177322
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Authors:Mengyu Fu, Kiril Solovey, Oren Salzman, Ron Alterovitz Abstract: The International Journal of Robotics Research, Ahead of Print. Medical steerable needles can follow 3D curvilinear trajectories to avoid anatomical obstacles and reach clinically significant targets inside the human body. Automating steerable needle procedures can enable physicians and patients to harness the full potential of steerable needles by maximally leveraging their steerability to safely and accurately reach targets for medical procedures such as biopsies. For the automation of medical procedures to be clinically accepted, it is critical from a patient care, safety, and regulatory perspective to certify the correctness and effectiveness of the planning algorithms involved in procedure automation. In this paper, we take an important step toward creating a certifiable optimal planner for steerable needles. We present an efficient, resolution-complete motion planner for steerable needles based on a novel adaptation of multi-resolution planning. This is the first motion planner for steerable needles that guarantees to compute in finite time an obstacle-avoiding plan (or notify the user that no such plan exists), under clinically appropriate assumptions. Based on this planner, we then develop the first resolution-optimal motion planner for steerable needles that further provides theoretical guarantees on the quality of the computed motion plan, that is, global optimality, in finite time. Compared to state-of-the-art steerable needle motion planners, we demonstrate with clinically realistic simulations that our planners not only provide theoretical guarantees but also have higher success rates, have lower computation times, and result in higher quality plans. Citation: The International Journal of Robotics Research PubDate: 2023-05-20T01:57:56Z DOI: 10.1177/02783649231165818
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Authors:Eric Huang, Xianyi Cheng, Yuemin Mao, Arnav Gupta, Matthew T Mason Abstract: The International Journal of Robotics Research, Ahead of Print. The central theme in robotic manipulation is that of the robot interacting with the world through physical contact. We tend to describe that physical contact using specific words that capture the nature of the contact and the action, such as grasp, roll, pivot, push, pull, tilt, close, open etc. We refer to these situation-specific actions as manipulation primitives. Due to the nonlinear and nonsmooth nature of physical interaction, roboticists have devoted significant efforts towards studying individual manipulation primitives. However, studying individual primitives one by one is an inherently limited process, due engineering costs, overfitting to specific tasks, and lack of robustness to unforeseen variations. These limitations motivate the main contribution of this paper: a complete and general framework to autogenerate manipulation primitives. To do so, we develop the theory and computation of contact modes as a means to classify and enumerate manipulation primitives. The contact modes form a graph, specifically a lattice. Our algorithm to autogenerate manipulation primitives (AMP) performs graph-based optimization on the contact mode lattice and solves a linear program to generate each primitive. We designed several experiments to validate our approach. We benchmarked a wide range of contact scenarios and our pipeline’s runtime was consistently in the 10 s of milliseconds. In simulation, we planned manipulation sequences using AMP. In the real-world, we showcased the robustness of our approach to real-world modeling errors. We hope that our contributions will lead to more general and robust approaches for robotic manipulation. Citation: The International Journal of Robotics Research PubDate: 2023-05-17T08:36:51Z DOI: 10.1177/02783649231170897
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Authors:Brad Saund, Sanjiban Choudhury, Siddhartha Srinivasa, Dmitry Berenson Abstract: The International Journal of Robotics Research, Ahead of Print. We address the problem of robot motion planning under uncertainty where the only observations are through contact with the environment. Such problems are typically solved by planning optimistically assuming unknown space is free, moving along the planned path and re-planning if the robot collides. However this approach can be very inefficient, leading to many unnecessary collisions and unproductive motion. We propose a new formulation, the Blindfolded Traveler’s Problem (BTP), for planning on a graph containing edges with unknown validity, with true validity observed only through attempted traversal by the robot. The solution to a BTP is a policy indicating the next edge to attempt given previous observations and an initial belief. We prove that BTP is NP-complete and show that exact modeling of the belief is intractable, therefore we present several approximation-based policies and beliefs. For the policy we propose graph search with edge weights augmented by the probability of collision. For the belief representation we propose a weighted Mixture of Experts of Collision Hypothesis Sets and a Manifold Particle Filter. Empirical evaluation in simulation and on a real robot arm shows that our proposed approach vastly outperforms several baselines as well as a previous approach that does not employ the BTP framework. Citation: The International Journal of Robotics Research PubDate: 2023-05-17T02:47:57Z DOI: 10.1177/02783649231170893
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Authors:Adam Pacheck, Steven James, George Konidaris, Hadas Kress-Gazit Abstract: The International Journal of Robotics Research, Ahead of Print. We present a framework for the automatic encoding and repair of high-level tasks. Given a set of skills a robot can perform, our approach first abstracts sensor data into symbols and then automatically encodes the robot’s capabilities in Linear Temporal Logic (LTL). Using this encoding, a user can specify reactive high-level tasks, for which we can automatically synthesize a strategy that executes on the robot, if the task is feasible. If a task is not feasible given the robot’s capabilities, we present two methods, one enumeration-based and one synthesis-based, for automatically suggesting additional skills for the robot or modifications to existing skills that would make the task feasible. We demonstrate our framework on a Baxter robot manipulating blocks on a table, a Baxter robot manipulating plates on a table, and a Kinova arm manipulating vials, with multiple sensor modalities, including raw images. Citation: The International Journal of Robotics Research PubDate: 2023-05-16T08:31:05Z DOI: 10.1177/02783649231167207
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Authors:Michael Lutter, Jan Peters Abstract: The International Journal of Robotics Research, Ahead of Print. Deep learning has been widely used within learning algorithms for robotics. One disadvantage of deep networks is that these networks are black-box representations. Therefore, the learned approximations ignore the existing knowledge of physics or robotics. Especially for learning dynamics models, these black-box models are not desirable as the underlying principles are well understood and the standard deep networks can learn dynamics that violate these principles. To learn dynamics models with deep networks that guarantee physically plausible dynamics, we introduce physics-inspired deep networks that combine first principles from physics with deep learning. We incorporate Lagrangian mechanics within the model learning such that all approximated models adhere to the laws of physics and conserve energy. Deep Lagrangian Networks (DeLaN) parametrize the system energy using two networks. The parameters are obtained by minimizing the squared residual of the Euler–Lagrange differential equation. Therefore, the resulting model does not require specific knowledge of the individual system, is interpretable, and can be used as a forward, inverse, and energy model. Previously these properties were only obtained when using system identification techniques that require knowledge of the kinematic structure. We apply DeLaN to learning dynamics models and apply these models to control simulated and physical rigid body systems. The results show that the proposed approach obtains dynamics models that can be applied to physical systems for real-time control. Compared to standard deep networks, the physics-inspired models learn better models and capture the underlying structure of the dynamics. Citation: The International Journal of Robotics Research PubDate: 2023-04-19T08:57:23Z DOI: 10.1177/02783649231169492
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Authors:Fadri Furrer, Tonci Novkovic, Marius Fehr, Margarita Grinvald, Cesar Cadena, Juan Nieto, Roland Siegwart Abstract: The International Journal of Robotics Research, Ahead of Print. The capabilities of discovering new knowledge and updating the previously acquired one are crucial for deploying autonomous robots in unknown and changing environments. Spatial and objectness concepts are at the basis of several robotic functionalities and are part of the intuitive understanding of the physical world for us humans. In this paper, we propose a method, which we call Modelify, to incrementally map the environment at the level of objects in a consistent manner. We follow an approach where no prior knowledge of the environment is required. The only assumption we make is that objects in the environment are separated by concave boundaries. The approach works on an RGB-D camera stream, where object-like segments are extracted and stored in an incremental database. Segment description and matching are performed by exploiting 2D and 3D information, allowing to build a graph of all segments. Finally, a matching score guides a Markov clustering algorithm to merge segments, thus completing object representations. Our approach allows creating single (merged) instances of repeating objects, objects that were observed from different viewpoints, and objects that were observed in previous mapping sessions. Thanks to our matching and merging strategies this also works with only partially overlapping segments. We perform evaluations on indoor and outdoor datasets recorded with different RGB-D sensors and show the benefit of using a clustering method to form merge candidates and keypoints detected in both 2D and 3D. Our new method shows better results than previous approaches while being significantly faster. A newly recorded dataset and the source code are released with this publication. Citation: The International Journal of Robotics Research PubDate: 2023-04-19T08:55:22Z DOI: 10.1177/02783649231166977
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Authors:Vincent Wall, Gabriel Zöller, Oliver Brock Abstract: The International Journal of Robotics Research, Ahead of Print. We propose a sensorization method for soft pneumatic actuators that uses an embedded microphone and speaker to measure different actuator properties. The physical state of the actuator determines the specific modulation of sound as it travels through the structure. Using simple machine learning, we create a computational sensor that infers the corresponding state from sound recordings. We demonstrate the acoustic sensor on a soft pneumatic continuum actuator and use it to measure contact locations, contact forces, object materials, actuator inflation, and actuator temperature. We show that the sensor is reliable (average classification rate for six contact locations of 93%), precise (mean spatial accuracy of 3.7 mm), and robust against common disturbances like background noise. Finally, we compare different sounds and learning methods and achieve best results with 20 ms of white noise and a support vector classifier as the sensor model. Citation: The International Journal of Robotics Research PubDate: 2023-04-15T12:58:35Z DOI: 10.1177/02783649231168954
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Authors:Krishan Rana, Vibhavari Dasagi, Jesse Haviland, Ben Talbot, Michael Milford, Niko Sünderhauf Abstract: The International Journal of Robotics Research, Ahead of Print. We present Bayesian Controller Fusion (BCF): a hybrid control strategy that combines the strengths of traditional hand-crafted controllers and model-free deep reinforcement learning (RL). BCF thrives in the robotics domain, where reliable but suboptimal control priors exist for many tasks, but RL from scratch remains unsafe and data-inefficient. By fusing uncertainty-aware distributional outputs from each system, BCF arbitrates control between them, exploiting their respective strengths. We study BCF on two real-world robotics tasks involving navigation in a vast and long-horizon environment, and a complex reaching task that involves manipulability maximisation. For both these domains, simple handcrafted controllers exist that can solve the task at hand in a risk-averse manner but do not necessarily exhibit the optimal solution given limitations in analytical modelling, controller miscalibration and task variation. As exploration is naturally guided by the prior in the early stages of training, BCF accelerates learning, while substantially improving beyond the performance of the control prior, as the policy gains more experience. More importantly, given the risk-aversity of the control prior, BCF ensures safe exploration and deployment, where the control prior naturally dominates the action distribution in states unknown to the policy. We additionally show BCF’s applicability to the zero-shot sim-to-real setting and its ability to deal with out-of-distribution states in the real world. BCF is a promising approach towards combining the complementary strengths of deep RL and traditional robotic control, surpassing what either can achieve independently. The code and supplementary video material are made publicly available at https://krishanrana.github.io/bcf. Citation: The International Journal of Robotics Research PubDate: 2023-04-07T12:30:56Z DOI: 10.1177/02783649231167210
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Authors:Bernardo Aceituno-Cabezas, Jose Ballester, Alberto Rodriguez Abstract: The International Journal of Robotics Research, Ahead of Print. This paper studies the robustness of grasping in the frictionless plane from a geometric perspective. By treating grasping as a process that shapes the free-space object over time, we define three types of certificates to guarantee success of a grasp: (a) invariance under an initial set, (b) convergence toward a goal grasp, and (c) observability over the final object pose. We develop convex-combinatorial models for each of these certificates, which can be expressed as simple semi-algebraic relations under mild-modeling assumptions, such as point-fingers and frictionless contact. By leveraging these models to synthesize certificates, we optimize certifiable grasps of planar objects composed as a union of convex polygons, using manipulators described as point-fingers. We validate this approach in simulations by grasping random polygons, and with real sensorless grasps of several objects. Citation: The International Journal of Robotics Research PubDate: 2023-04-03T03:46:54Z DOI: 10.1177/02783649231155952
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Authors:Sihui Li, Neil T. Dantam Abstract: The International Journal of Robotics Research, Ahead of Print. We present a learning-based approach to prove infeasibility of kinematic motion planning problems. Sampling-based motion planners are effective in high-dimensional spaces but are only probabilistically complete. Consequently, these planners cannot provide a definite answer if no plan exists, which is important for high-level scenarios, such as task-motion planning. We apply data generated during multi-directional sampling-based planning (such as PRM) to a machine learning approach to construct an infeasibility proof. An infeasibility proof is a closed manifold in the obstacle region of the configuration space that separates the start and goal into disconnected components of the free configuration space. We train the manifold using common machine learning techniques and then triangulate the manifold into a polytope to prove containment in the obstacle region. Under assumptions about the hyper-parameters and robustness of configuration space optimization, the output is either an infeasibility proof or a motion plan in the limit. We demonstrate proof construction for up to 4-DOF configuration spaces. A large part of the algorithm is parallelizable, which offers potential to address higher dimensional configuration spaces. Citation: The International Journal of Robotics Research PubDate: 2023-02-03T07:19:30Z DOI: 10.1177/02783649231154674
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Authors:Jingjin Yu Abstract: The International Journal of Robotics Research, Ahead of Print. We study a class of rearrangement problems under a novel pick-n-swap prehensile manipulation model, in which a robotic manipulator, capable of carrying an item and making item swaps, is tasked to sort items stored in lattices of variable dimensions in a time-optimal manner. We systematically analyze the intrinsic optimality structure, which is fairly rich and intriguing, under different levels of item distinguishability (fully-labeled, where each item has a unique label, or partially-labeled, where multiple items may be of the same type) and different lattice dimensions. Focusing on the most practical setting of one and two dimensions, we develop low polynomial time cycle-following-based algorithms that optimally perform rearrangements on 1D lattices under both fully- and partially-labeled settings. On the other hand, we show that rearrangement on 2D and higher-dimensional lattices become computationally intractable to optimally solve. Despite their NP-hardness, we prove that efficient cycle-following-based algorithms remain optimal in the asymptotic sense for 2D fully- and partially-labeled settings, in expectation, using the interesting fact that random permutations induce only a small number of cycles. We further improve these algorithms to provide 1.x-optimality when the number of items is small. Simulation studies corroborate the effectiveness of our algorithms. The implementation of the algorithms from the paper can be found at github.com/arc-l/lattice-rearrangement. Citation: The International Journal of Robotics Research PubDate: 2023-02-02T01:47:36Z DOI: 10.1177/02783649231153901
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Authors:Moju Zhao, Kei Okada, Masayuki Inaba Abstract: The International Journal of Robotics Research, Ahead of Print. Various state-of-the-art works have achieved aerial manipulation and grasping by attaching additional manipulator to aerial robots. However, such a coupled platform has limitations with respect to the interaction force and mobility. In this paper, we present the successful implementation of aerial manipulation and grasping by a novel articulated aerial robot called DRAGON, in which a vectorable rotor unit is embedded in each link. The key to performing stable manipulation and grasping in the air is the usage of rotor vectoring apparatus having two degrees-of-freedom. First, a comprehensive flight control methodology for aerial transformation using the vectorable thrust force is developed with the consideration of the dynamics of vectoring actuators. This proposed control method can suppress the oscillation due to the dynamics of vectoring actuators and also allow the integration with external and internal wrenches for object manipulation and grasping. Second, an online thrust-level planning method for bimanual object grasping using the two ends of this articulated model is presented. The proposed grasping style is unique in that the vectorable thrust force is used as the internal wrench instead of the joint torque. Finally, we show the experimental results of evaluation on the proposed control and planning methods for object manipulation and grasping. Citation: The International Journal of Robotics Research PubDate: 2022-08-18T09:09:02Z DOI: 10.1177/02783649221112446
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Authors:Jaskaran Grover, Changliu Liu, Katia Sycara Abstract: The International Journal of Robotics Research, Ahead of Print. Collision avoidance for multi-robot systems is a well-studied problem. Recently, control barrier functions (CBFs) have been proposed for synthesizing controllers that guarantee collision avoidance and goal stabilization for multiple robots. However, it has been noted that reactive control synthesis methods (such as CBFs) are prone to deadlock, an equilibrium of system dynamics that causes the robots to stall before reaching their goals. In this paper, we analyze the closed-loop dynamics of robots using CBFs, to characterize controller parameters, initial conditions, and goal locations that invariably lead the system to deadlock. Using tools from duality theory, we derive geometric properties of robot configurations of an N robot system once it is in deadlock and we justify them using the mechanics interpretation of KKT conditions. Our key deductions are that (1) system deadlock is characterized by a force equilibrium on robots and (2) deadlock occurs to ensure safety when safety is at the brink of being violated. These deductions allow us to interpret deadlock as a subset of the state space, and we show that this set is non-empty and located on the boundary of the safe set. By exploiting these properties, we analyze the number of admissible robot configurations in deadlock and develop a provably correct decentralized algorithm for deadlock resolution to safely deliver the robots to their goals. This algorithm is validated in simulations as well as experimentally on Khepera-IV robots. For an interactive version of this paper, please visit https://tinyurl.com/229tpssp Citation: The International Journal of Robotics Research PubDate: 2022-07-09T08:09:21Z DOI: 10.1177/02783649221074718
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Authors:Marcus Hoerger, Hanna Kurniawati, Alberto Elfes Abstract: The International Journal of Robotics Research, Ahead of Print. Planning under partial observability is essential for autonomous robots. A principled way to address such planning problems is the Partially Observable Markov Decision Process (POMDP). Although solving POMDPs is computationally intractable, substantial advancements have been achieved in developing approximate POMDP solvers in the past two decades. However, computing robust solutions for systems with complex dynamics remains challenging. Most on-line solvers rely on a large number of forward simulations and standard Monte Carlo methods to compute the expected outcomes of actions the robot can perform. For systems with complex dynamics, for example, those with non-linear dynamics that admit no closed-form solution, even a single forward simulation can be prohibitively expensive. Of course, this issue exacerbates for problems with long planning horizons. This paper aims to alleviate the above difficulty. To this end, we propose a new on-line POMDP solver, called Multilevel POMDP Planner (MLPP), that combines the commonly known Monte-Carlo-Tree-Search with the concept of Multilevel Monte Carlo to speed up our capability in generating approximately optimal solutions for POMDPs with complex dynamics. Experiments on four different problems involving torque control, navigation and grasping indicate that MLPP substantially outperforms state-of-the-art POMDP solvers. Citation: The International Journal of Robotics Research PubDate: 2022-06-13T04:59:04Z DOI: 10.1177/02783649221093658
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Authors:Allison Pinosky, Ian Abraham, Alexander Broad, Brenna Argall, Todd D Murphey Abstract: The International Journal of Robotics Research, Ahead of Print. We develop an approach to improve the learning capabilities of robotic systems by combining learned predictive models with experience-based state-action policy mappings. Predictive models provide an understanding of the task and the dynamics, while experience-based (model-free) policy mappings encode favorable actions that override planned actions. We refer to our approach of systematically combining model-based and model-free learning methods as hybrid learning. Our approach efficiently learns motor skills and improves the performance of predictive models and experience-based policies. Moreover, our approach enables policies (both model-based and model-free) to be updated using any off-policy reinforcement learning method. We derive a deterministic method of hybrid learning by optimally switching between learning modalities. We adapt our method to a stochastic variation that relaxes some of the key assumptions in the original derivation. Our deterministic and stochastic variations are tested on a variety of robot control benchmark tasks in simulation as well as a hardware manipulation task. We extend our approach for use with imitation learning methods, where experience is provided through demonstrations, and we test the expanded capability with a real-world pick-and-place task. The results show that our method is capable of improving the performance and sample efficiency of learning motor skills in a variety of experimental domains. Citation: The International Journal of Robotics Research PubDate: 2022-06-02T12:08:13Z DOI: 10.1177/02783649221083331
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Authors:Karen Leung, Nikos Aréchiga, Marco Pavone Abstract: The International Journal of Robotics Research, Ahead of Print. This paper presents a technique, named STLCG, to compute the quantitative semantics of Signal Temporal Logic (STL) formulas using computation graphs. STLCG provides a platform which enables the incorporation of logical specifications into robotics problems that benefit from gradient-based solutions. Specifically, STL is a powerful and expressive formal language that can specify spatial and temporal properties of signals generated by both continuous and hybrid systems. The quantitative semantics of STL provide a robustness metric, that is, how much a signal satisfies or violates an STL specification. In this work, we devise a systematic methodology for translating STL robustness formulas into computation graphs. With this representation, and by leveraging off-the-shelf automatic differentiation tools, we are able to efficiently backpropagate through STL robustness formulas and hence enable a natural and easy-to-use integration of STL specifications with many gradient-based approaches used in robotics. Through a number of examples stemming from various robotics applications, we demonstrate that STLCG is versatile, computationally efficient, and capable of incorporating human-domain knowledge into the problem formulation. Citation: The International Journal of Robotics Research PubDate: 2022-05-28T04:04:49Z DOI: 10.1177/02783649221082115
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Authors:Grace McFassel, Dylan A Shell Abstract: The International Journal of Robotics Research, Ahead of Print. Typically to a roboticist, a plan is the outcome of other work, a synthesized object that realizes ends defined by some problem; plans qua plans are seldom treated as first-class objects of study. Plans designate functionality: a plan can be viewed as defining a robot’s behavior throughout its execution. This informs and reveals many other aspects of the robot’s design, including: necessary sensors and action choices, history, state, task structure, and how to define progress. Interrogating sets of plans helps in comprehending the ways in which differing executions influence the interrelationships between these various aspects. Revisiting Erdmann’s theory of action-based sensors, a classical approach for characterizing fundamental information requirements, we show how plans (in their role of designating behavior) influence sensing requirements. Using an algorithm for enumerating plans, we examine how some plans for which no action-based sensor exists can be transformed into sets of sensors through the identification and handling of features that preclude the existence of action-based sensors. We are not aware of those obstructing features having been previously identified. Action-based sensors may be treated as standalone reactive plans; we relate them to the set of all possible plans through a lattice structure. This lattice reveals a boundary between plans with action-based sensors and those without. Some plans, specifically those that are not reactive plans and require some notion of internal state, can never have associated action-based sensors. Even so, action-based sensors can serve as a framework to explore and interpret how such plans make use of state. Citation: The International Journal of Robotics Research PubDate: 2022-05-12T11:49:39Z DOI: 10.1177/02783649221078874
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Authors:Nicholas Collins, Hanna Kurniawati Abstract: The International Journal of Robotics Research, Ahead of Print. End-to-end learning for planning is a promising approach for finding good robot strategies in situations where the state transition, observation, and reward functions are initially unknown. Many neural network architectures for this approach have shown positive results. Across these networks, seemingly small components have been used repeatedly in different architectures, which means improving the efficiency of these components has great potential to improve the overall performance of the network. This paper aims to improve one such component: The forward propagation module. In particular, we propose Locally Connected Interrelated Network (LCI-Net) – a novel type of locally connected layer with unshared but interrelated weights – to improve the efficiency of learning stochastic transition models for planning and propagating information via the learned transition models. LCI-Net is a small differentiable neural network module that can be plugged into various existing architectures. For evaluation purposes, we apply LCI-Net to VIN and QMDP-Net. VIN is an end-to-end neural network for solving Markov Decision Processes (MDPs) whose transition and reward functions are initially unknown, while QMDP-Net is its counterpart for the Partially Observable Markov Decision Process (POMDP) whose transition, observation, and reward functions are initially unknown. Simulation tests on benchmark problems involving 2D and 3D navigation and grasping indicate promising results: Changing only the forward propagation module alone with LCI-Net improves VIN’s and QMDP-Net generalisation capability by more than 3× and 10×, respectively. Citation: The International Journal of Robotics Research PubDate: 2022-05-12T01:54:51Z DOI: 10.1177/02783649221093092
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Authors:Zherong Pan, Min Liu, Xifeng Gao, Dinesh Manocha Abstract: The International Journal of Robotics Research, Ahead of Print. We present an algorithm to compute planar linkage topology and geometry, given a user-specified end-effector trajectory. Planar linkage structures convert rotational or prismatic motions of a single actuator into an arbitrarily complex periodic motion, which is an important component when building low-cost, modular robots, mechanical toys, and foldable structures in our daily lives (chairs, bikes, and shelves). The design of such structures requires trial and error even for experienced engineers. Our research provides semi-automatic methods for exploring novel designs given high-level specifications and constraints. We formulate this problem as a non-smooth numerical optimization with quadratic objective functions and non-convex quadratic constraints involving mixed-integer decision variables (MIQCQP). We propose and compare three approximate algorithms to solve this problem: mixed-integer conic-programming (MICP), mixed-integer nonlinear programming (MINLP), and simulated annealing (SA). We evaluated these algorithms searching for planar linkages involving 10 − 14 rigid links. Our results show that the best performance can be achieved by combining MICP and MINLP, leading to a hybrid algorithm capable of finding the planar linkages within a couple of hours on a desktop machine, which significantly outperforms the SA baseline in terms of optimality. We highlight the effectiveness of our optimized planar linkages by using them as legs of a walking robot. Citation: The International Journal of Robotics Research PubDate: 2022-03-28T03:26:54Z DOI: 10.1177/02783649211069156
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Authors:Hazhar Rahmani, Dylan A Shell, Jason M O’Kane Abstract: The International Journal of Robotics Research, Ahead of Print. One important class of applications entails a robot scrutinizing, monitoring, or recording the evolution of an uncertain time-extended process. This sort of situation leads to an interesting family of active perception problems that can be cast as planning problems in which the robot is limited in what it sees and must, thus, choose what to pay attention to. The distinguishing characteristic of this setting is that the robot has influence over what it captures via its sensors, but exercises no causal authority over the process evolving in the world. As such, the robot’s objective is to observe the underlying process and to produce a “chronicle” of occurrent events, subject to a goal specification of the sorts of event sequences that may be of interest. This paper examines variants of such problems in which the robot aims to collect sets of observations to meet a rich specification of their sequential structure. We study this class of problems by modeling a stochastic process via a variant of a hidden Markov model and specify the event sequences of interest as a regular language, developing a vocabulary of “mutators” that enable sophisticated requirements to be expressed. Under different suppositions on the information gleaned about the event model, we formulate and solve different planning problems. The core underlying idea is the construction of a product between the event model and a specification automaton. Using this product, we compute a policy that minimizes the expected number of steps to reach a goal state. We introduce a general algorithm for this problem as well as several more efficient algorithms for important special cases. The paper reports and compares performance metrics by drawing on some small case studies analyzed in depth via simulation. Specifically, we study the effect of the robot’s observation model on the average time required for the robot to record a desired story. We also compare our algorithm with a baseline greedy algorithm, showing that our algorithm outperforms the greedy algorithm in terms of the average time to record a desired story. In addition, experiments show that the algorithms tailored to specialized variants of the problem are rather more efficient than the general algorithm. Citation: The International Journal of Robotics Research PubDate: 2022-03-28T01:18:27Z DOI: 10.1177/02783649211069154
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Authors:Mario Szegedy, Jingjin Yu Abstract: The International Journal of Robotics Research, Ahead of Print. A great number of robotics applications demand the rearrangement of many mobile objects, for example, organizing products on store shelves, shuffling containers at shipping ports, reconfiguring fleets of mobile robots, and so on. To boost the efficiency/throughput in systems designed for solving these rearrangement problems, it is essential to minimize the number of atomic operations that are involved, for example, the pick-n-places of individual objects. However, this optimization task poses a rather difficult challenge due to the complex inter-dependency between the objects, especially when they are tightly packed together. In this work, in tackling the aforementioned challenges, we have developed a novel algorithmic tool, called Rubik Tables, that provides a clean abstraction of object rearrangement problems as the proxy problem of shuffling items stored in a table or lattice. In its basic form, a Rubik Table is an n × n table containing n2 items. We show that the reconfiguration of items in such a Rubik Table can be achieved using at most n column and n row shuffles in the partially labeled setting, where each column (resp., row) shuffle may arbitrarily permute the items stored in a column (resp., row) of the table. When items are fully distinguishable, additional n shuffles are needed. Rubik Tables allow many generalizations, for example, adding an additional depth dimension or extending to higher dimensions. Using Rubik Table results, we have designed a first constant-factor optimal algorithm for stack rearrangement problems where items are stored in stacks, accessible only from the top. We show that, for nd items stored in n stacks of depth d each, using one empty stack as the swap space, O(nd) stack pop-push operations are sufficient for an arbitrary reconfiguration of the stacks where [math] for arbitrary fixed m> 0. Rubik Table results also allow the development of constant-factor optimal solutions for solving multi-robot motion planning problems under extreme robot density. These algorithms based on Rubik Table results run in low-polynomial time. Citation: The International Journal of Robotics Research PubDate: 2022-03-01T05:53:58Z DOI: 10.1177/02783649211059844
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