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Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning.
Caleb Chuck, Carl Qi, Michael J. Munje, Shuozhe Li, Max Rudolph, Chang Shi, Siddhant Agarwal, Harshit Sikchi,
Abhinav Peri, Sarthak Dayal, Evan Kuo, Kavan Mehta, Anthony Wang, Peter Stone,
Amy Zhang, and Scott Niekum.
In ICRA Workshop on Manipulation Skills,
May 2024.
Reinforcement Learning is a promising tool for learning complex policies even in fast-moving and object-interactive domains where human teleoperation or hard-coded policies might fail. To effectively reflect this challenging category of tasks, we introduce a dynamic, interactive RL testbed based on robot air hockey. By augmenting air hockey with a large family of tasks ranging from easy tasks like reaching, to challenging ones like pushing a block by hitting it with a puck, as well as goal-based and human-interactive tasks, our testbed allows a varied assessment of RL capabilities. The robot air hockey testbed also supports sim-to-real transfer with three domains: two simulators of increasing fidelity and a real robot system. Using a dataset of demonstration data gathered through two teleoperation systems: a virtualized control environment, and human shadowing, we assess the testbed with behavior cloning, offline RL, and RL from scratch.
@InProceedings{caleb_chuck_AirHockeyICRA_2024,
author = {Caleb Chuck and Carl Qi and Michael J. Munje and Shuozhe Li and Max Rudolph and Chang Shi and Siddhant Agarwal and Harshit Sikchi and Abhinav Peri and Sarthak Dayal and Evan Kuo and Kavan Mehta and Anthony Wang and Peter Stone and Amy Zhang and Scott Niekum},
title = {Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning},
booktitle = {ICRA Workshop on Manipulation Skills},
year = {2024},
month = {May},
location = {Yokohama, Japan},
abstract = {Reinforcement Learning is a promising tool for learning complex policies even in fast-moving and object-interactive domains where human teleoperation or hard-coded policies might fail. To effectively reflect this challenging category of tasks, we introduce a dynamic, interactive RL testbed based on robot air hockey. By augmenting air hockey with a large family of tasks ranging from easy tasks like reaching, to challenging ones like pushing a block by hitting it with a puck, as well as goal-based and human-interactive tasks, our testbed allows a varied assessment of RL capabilities. The robot air hockey testbed also supports sim-to-real transfer with three domains: two simulators of increasing fidelity and a real robot system. Using a dataset of demonstration data gathered through two teleoperation systems: a virtualized control environment, and human shadowing, we assess the testbed with behavior cloning, offline RL, and RL from scratch.},
}
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