• DOI:10.1109/IROS40897.2019.8968201
Corpus ID: 195316875

Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks

@article{MartnMartn2019VariableIC,
  title={Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks},
  author={Roberto Mart{\'i}n-Mart{\'i}n and M. Lee and Rachel Gardner and S. Savarese and Jeannette Bohg and Animesh Garg},
  journal={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2019},
  pages={1010-1017}
}

Roberto Martín-Martín, M. Lee, +3 authors Animesh Garg

Published 2019

Computer Science

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Reinforcement Learning (RL) of contact-rich manipulation tasks has yielded impressive results in recent years. While many studies in RL focus on varying the observation space or reward model, few efforts focused on the choice of action space (e.g. joint or end-effector space, position, velocity, etc.). However, studies in robot motion control indicate that choosing an action space that conforms to the characteristics of the task can simplify exploration and improve robustness to disturbances… CONTINUE READING

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