PokeRRT: Poking as a Skill and Failure Recovery Tactic for Planar Non-Prehensile Manipulation

Pasricha, Anuj; Tung, Yi-Shiuan; Hayes, Bradley; Roncone, Alessandro

doi:10.1109/lra.2022.3148442

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Looking at additional examples in the legged locomotion (59,60) or nonprehensile manipulation (61,62) literature, it is evident that this family of multicontact planners has been predominantly applied in the quasi-static domain. One way of extending them is via prespecified dynamic motion primitives (63,64). However, such methods do not generalize well because they might require extra design effort to generate unique high-level primitives given a new task.…”

Section: Introductionmentioning

confidence: 99%

Versatile multicontact planning and control for legged loco-manipulation

2023

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Loco-manipulation planning skills are pivotal for expanding the utility of robots in everyday environments. These skills can be assessed on the basis of a system’s ability to coordinate complex holistic movements and multiple contact interactions when solving different tasks. However, existing approaches have been merely able to shape such behaviors with hand-crafted state machines, densely engineered rewards, or prerecorded expert demonstrations. Here, we propose a minimally guided framework that automatically discovers whole-body trajectories jointly with contact schedules for solving general loco-manipulation tasks in premodeled environments. The key insight is that multimodal problems of this nature can be formulated and treated within the context of integrated task and motion planning (TAMP). An effective bilevel search strategy was achieved by incorporating domain-specific rules and adequately combining the strengths of different planning techniques: trajectory optimization and informed graph search coupled with sampling-based planning. We showcase emergent behaviors for a quadrupedal mobile manipulator exploiting both prehensile and nonprehensile interactions to perform real-world tasks such as opening/closing heavy dishwashers and traversing spring-loaded doors. These behaviors were also deployed on the real system using a two-layer whole-body tracking controller.

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Section: Introductionmentioning

confidence: 99%

Versatile multicontact planning and control for legged loco-manipulation

2023

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“…However, it is difficult to predict the motion of the object, and thus a robot should reposition its pushing point every second to transport an object to a goal. Because of these characteristics, prehensile manipulation is an appropriate method for structured objects (i.e., known objects) [ 13 ] and non-prehensile manipulation is applicable to unstructured objects (i.e., unknown objects) [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Deep-Reinforcement-Learning-Based Object Transportation Using Task Space Decomposition

Eoh

2023

Sensors

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This paper presents a novel object transportation method using deep reinforcement learning (DRL) and the task space decomposition (TSD) method. Most previous studies on DRL-based object transportation worked well only in the specific environment where a robot learned how to transport an object. Another drawback was that DRL only converged in relatively small environments. This is because the existing DRL-based object transportation methods are highly dependent on learning conditions and training environments; they cannot be applied to large and complicated environments. Therefore, we propose a new DRL-based object transportation that decomposes a difficult task space to be transported into simple multiple sub-task spaces using the TSD method. First, a robot sufficiently learned how to transport an object in a standard learning environment (SLE) that has small and symmetric structures. Then, a whole-task space was decomposed into several sub-task spaces by considering the size of the SLE, and we created sub-goals for each sub-task space. Finally, the robot transported an object by sequentially occupying the sub-goals. The proposed method can be extended to a large and complicated new environment as well as the training environment without additional learning or re-learning. Simulations in different environments are presented to verify the proposed method, such as a long corridor, polygons, and a maze.

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The Virtues of Laziness: Multi-Query Kinodynamic Motion Planning with Lazy Methods

Pasricha,

Roncone

2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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PokeRRT: Poking as a Skill and Failure Recovery Tactic for Planar Non-Prehensile Manipulation

Cited by 5 publications

References 23 publications

Versatile multicontact planning and control for legged loco-manipulation

Versatile multicontact planning and control for legged loco-manipulation

Deep-Reinforcement-Learning-Based Object Transportation Using Task Space Decomposition

The Virtues of Laziness: Multi-Query Kinodynamic Motion Planning with Lazy Methods

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