Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments

no code implementations • 22 Oct 2020 • Jun Yamada, Youngwoon Lee, Gautam Salhotra, Karl Pertsch, Max Pflueger, Gaurav S. Sukhatme, Joseph J. Lim, Peter Englert

In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goals, but suffer from inaccurate models in tasks that require contacts with the environment.

reinforcement-learning Reinforcement Learning (RL) +1

Plan-Space State Embeddings for Improved Reinforcement Learning

no code implementations • 30 Apr 2020 • Max Pflueger, Gaurav S. Sukhatme

We show how these embedding spaces can then be used as an augmentation to the robot state in reinforcement learning problems.

reinforcement-learning Reinforcement Learning (RL)

Soft Value Iteration Networks for Planetary Rover Path Planning

no code implementations • ICLR 2018 • Max Pflueger, Ali Agha, Gaurav S. Sukhatme

In order to deal with complex terrain observations and policy learning, we propose a value iteration recurrence, referred to as the soft value iteration network (SVIN).

Motion Planning