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SofaGym: An Open Platform for Reinforcement Learning Based on Soft Robot Simulations.

Pierre Schegg1,2, Etienne Ménager1, Elie Khairallah1

  • 1Inria, CNRS, Centrale Lille, UMR 9189 CRIStAL, Univ. Lille, Lille, France.

Soft Robotics
|December 8, 2022
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Summary
This summary is machine-generated.

SofaGym is a new open-source software enabling the creation of OpenAI Gym environments for soft robot digital twins. This platform facilitates reinforcement learning (RL) research by coupling RL algorithms with physics-based soft robot simulations.

Keywords:
Finite Element MethodMonte Carlo Tree SearchOpenAI GymReinforcement LearningSOFAimitation learning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • OpenAI Gym is a standard interface for training Reinforcement Learning (RL) algorithms.
  • The Simulation Open Framework Architecture (SOFA) is a physics-based engine for soft robotics simulation and control.
  • Coupling soft robotics with RL presents unique challenges in representation, environmental interaction, and policy transfer.

Purpose of the Study:

  • To introduce SofaGym, an open-source software for creating OpenAI Gym environments from soft robot digital twins.
  • To provide a platform for exploring the challenges and opportunities at the intersection of soft robotics and RL.
  • To facilitate research in applying RL and planning algorithms to physics-based soft robot simulations.

Main Methods:

  • Development of SofaGym, an open-source software bridging SOFA and OpenAI Gym.
  • Creation of 11 diverse environments representing various soft robots and applications.
  • Demonstration of methods using traditional control, RL, and planning algorithms within the SofaGym framework.

Main Results:

  • SofaGym enables the creation of OpenAI Gym-compatible environments for soft robot simulations.
  • The platform supports the investigation of challenges such as robot representation, complex interactions, and sim-to-real transfer.
  • Eleven distinct environments are presented, showcasing a range of soft robot applications and associated challenges.

Conclusions:

  • SofaGym offers novel possibilities for integrating RL with physics-based soft robot simulations.
  • The platform serves as a valuable community resource for advancing research in soft robotics and RL.
  • SofaGym facilitates the exploration of RL and planning strategies for controlling and understanding soft robots.