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Learning {Force} {Control} {Policies} for {Compliant} {Manipulation}


Conference Paper



Developing robots capable of fine manipulation skills is of major importance in order to build truly assistive robots. These robots need to be compliant in their actuation and control in order to operate safely in human environments. Manipulation tasks imply complex contact interactions with the external world, and involve reasoning about the forces and torques to be applied. Planning under contact conditions is usually impractical due to computational complexity, and a lack of precise dynamics models of the environment. We present an approach to acquiring manipulation skills on compliant robots through reinforcement learning. The initial position control policy for manipulation is initialized through kinesthetic demonstration. We augment this policy with a force/torque profile to be controlled in combination with the position trajectories. We use the Policy Improvement with Path Integrals (PI2) algorithm to learn these force/torque profiles by optimizing a cost function that measures task success. We demonstrate our approach on the Barrett WAM robot arm equipped with a 6-DOF force/torque sensor on two different manipulation tasks: opening a door with a lever door handle, and picking up a pen off the table. We show that the learnt force control policies allow successful, robust execution of the tasks.

Author(s): Kalakrishnan, M. and Righetti, L. and Pastor, P. and Schaal, S.
Book Title: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
Pages: 4639--4644
Year: 2011
Month: sep
Publisher: IEEE

Department(s): Autonomous Motion, Movement Generation and Control
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/IROS.2011.6095096

Address: San Francisco, USA
URL: https://ieeexplore.ieee.org/abstract/document/6095096/


  title = {Learning {Force} {Control} {Policies} for {Compliant} {Manipulation}},
  author = {Kalakrishnan, M. and Righetti, L. and Pastor, P. and Schaal, S.},
  booktitle = {2011 {IEEE}/{RSJ} {International} {Conference} on {Intelligent} {Robots} and {Systems}},
  pages = {4639--4644},
  publisher = {IEEE},
  address = {San Francisco, USA},
  month = sep,
  year = {2011},
  url = {https://ieeexplore.ieee.org/abstract/document/6095096/},
  month_numeric = {9}