Imitation Learning in POMDPs with Contacts
Hai Nguyen, Xinchao Song, Christopher Amato, and Robert Platt. Robotics: Science and Systems (RSS 2020): Reacting to Contact Workshop. July 2020. [pdf]
In this paper, we use the access to full states during training and imitation learning to train an intelligent agent that uses contacts to explore to solve a manipulation task. We formulate the problem as a partially observable decision process (POMDP) and solve it using imitation learning. We train a POMDP expert that solves the task while performing informative actions using contacts as well as an agent that acts on partial information by cloning this expert’s behavior. We test our method on a novel robotics domain and set up an experiment with a real robot.