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Concurrent Policy Blending and System Identification for Generalized Assistive Control

arXiv 2205.09836 ↗First posted 19 May 2022cs.RO cs.AI cs.HCcited by 0

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Abstract (arXiv)

In this work, we address the problem of solving complex collaborative robotic tasks subject to multiple varying parameters. Our approach combines simultaneous policy blending with system identification to create generalized policies that are robust to changes in system parameters. We employ a blending network whose state space relies solely on parameter estimates from a system identification technique. As a result, this blending network learns how to handle parameter changes instead of trying to learn how to solve the task for a generalized parameter set simultaneously. We demonstrate our scheme's ability on a collaborative robot and human itching task in which the human has motor impairments. We then showcase our approach's efficiency with a variety of system identification techniques when compared to standard domain randomization.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 19 May 2022.

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References · 12

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  3. 2019Solving Rubik's Cube with a Robot Handcited by 734
  4. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 672
  5. 2018Sim-to-Real: Learning Agile Locomotion For Quadruped Robotscited by 319
  6. 2019Active Domain Randomizationcited by 170
  7. 2018Sim-to-Real Reinforcement Learning for Deformable Object Manipulationcited by 131
  8. 2021Auto-Tuned Sim-to-Real Transfercited by 49
  9. 2020Sim2Real Transfer for Reinforcement Learning without Dynamics Randomizationcited by 47
  10. 2019Assistive Gym: A Physics Simulation Framework for Assistive Roboticscited by 44
  11. 2019Learning to Collaborate from Simulation for Robot-Assisted Dressingcited by 16
  12. 2020Combining system identification with reinforcement learning-based MPCcited by 11

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