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.
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.
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).