RMPs for Safe Impedance Control in Contact-Rich Manipulation

Shaw, Seiji; Abbatematteo, Ben; Konidaris, George

doi:10.48550/arxiv.2109.12103

Cited by 1 publication

(2 citation statements)

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“…There has been a set of alternative works, dealing with different problems in Riemannian motion policies. In (Shaw et al, 2021) pullback bundle dynamical systems are proposed. The work proposes a method to combine multiple policies defined in non-Euclidean spaces.…”

Section: Multi-objective Reactive Motion Generationmentioning

confidence: 99%

“…Artificial potential fields (APF) methods (Ge and Cui, 2002; Khatib, 1985) and more recently Riemannian motion policies (RMP) methods (Aljalbout et al, 2021;Bylard et al, 2021;Cheng et al, 2018;Kappler et al, 2018;Ratliff et al, 2018;Shaw et al, 2021) are one of the most popular approaches for reactive motion generation in manipulators. In contrast with path planning or trajectory optimization methods, these methods propose to solve a myopic (onestep ahead) control problem.…”

Section: Introductionmentioning

confidence: 99%

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Composable energy policies for reactive motion generation and reinforcement learning

Urain

Liu

et al. 2023

The International Journal of Robotics Research

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In this work, we introduce composable energy policies (CEP), a novel framework for multi-objective motion generation. We frame the problem of composing multiple policy components from a probabilistic view. We consider a set of stochastic policies represented in arbitrary task spaces, where each policy represents a distribution of the actions to solve a particular task. Then, we aim to find the action in the configuration space that optimally satisfies all the policy components. The presented framework allows the fusion of motion generators from different sources: optimal control, data-driven policies, motion planning, and handcrafted policies. Classically, the problem of multi-objective motion generation is solved by the composition of a set of deterministic policies, rather than stochastic policies. However, there are common situations where different policy components have conflicting behaviors, leading to oscillations or the robot getting stuck in an undesirable state. While our approach is not directly able to solve the conflicting policies problem, we claim that modeling each policy as a stochastic policy allows more expressive representations for each component in contrast with the classical reactive motion generation approaches. In some tasks, such as reaching a target in a cluttered environment, we show experimentally that CEP additional expressivity allows us to model policies that reduce these conflicting behaviors. A field that benefits from these reactive motion generators is the one of robot reinforcement learning. Integrating these policy architectures with reinforcement learning allows us to include a set of inductive biases in the learning problem. These inductive biases guide the reinforcement learning agent towards informative regions or improve collision safety while exploring. In our work, we show how to integrate our proposed reactive motion generator as a structured policy for reinforcement learning. Combining the reinforcement learning agent exploration with the prior-based CEP, we can improve the learning performance and explore safer.

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Section: Multi-objective Reactive Motion Generationmentioning

confidence: 99%