Reinforcement Learning for Pivoting Task

In this work we propose an approach to learn a robust policy for solving the pivoting task. Recently, several model-free continuous control algorithms were shown to learn successful policies withou ...

[1]  Dean Karnopp,et al.  Computer simulation of stick-slip friction in mechanical dynamic systems , 1985 .

[2]  Jeffrey C. Trinkle,et al.  A framework for planning dexterous manipulation , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[3]  Carlos Canudas de Wit,et al.  Friction Models and Friction Compensation , 1998, Eur. J. Control.

[4]  Andrew W. Moore,et al.  Locally Weighted Learning for Control , 1997, Artificial Intelligence Review.

[5]  Masatoshi Ishikawa,et al.  Dynamic regrasping using a high-speed multifingered hand and a high-speed vision system , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[6]  Danica Kragic,et al.  Predicting slippage and learning manipulation affordances through Gaussian Process regression , 2013, 2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids).

[7]  Sergey Levine,et al.  Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics , 2014, NIPS.

[8]  Siddhartha S. Srinivasa,et al.  Extrinsic dexterity: In-hand manipulation with external forces , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Danica Kragic,et al.  In-hand manipulation using gravity and controlled slip , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[10]  Sergey Levine,et al.  Trust Region Policy Optimization , 2015, ICML.

[11]  Matthew T. Mason,et al.  A general framework for open-loop pivoting , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[12]  Alberto Rodriguez,et al.  A two-phase gripper to reorient and grasp , 2015, 2015 IEEE International Conference on Automation Science and Engineering (CASE).

[13]  Yuval Tassa,et al.  Continuous control with deep reinforcement learning , 2015, ICLR.

[14]  Pieter Abbeel,et al.  Benchmarking Deep Reinforcement Learning for Continuous Control , 2016, ICML.

[15]  Avishai Sintov,et al.  Swing-up regrasping algorithm using energy control , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[16]  Danica Kragic,et al.  Adaptive control for pivoting with visual and tactile feedback , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[17]  Wojciech Zaremba,et al.  OpenAI Gym , 2016, ArXiv.

[18]  Razvan Pascanu,et al.  Sim-to-Real Robot Learning from Pixels with Progressive Nets , 2016, CoRL.