273 citations · 633 across the 38 of their papers we have counts for
3 papers · 1 filter
Accelerating Reinforcement Learning with Learned Skill Priors
Karl Pertsch, Youngwoon Lee, Joseph J. Lim
Intelligent agents rely heavily on prior experience when learning a new task, yet most modern reinforcement learning (RL) approaches learn every task from scratch. One approach for…
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments
Jun Yamada, Youngwoon Lee, Gautam Salhotra +5
Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in envi…
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors
Karl Pertsch, Oleh Rybkin, Frederik Ebert +3
The ability to predict and plan into the future is fundamental for agents acting in the world. To reach a faraway goal, we predict trajectories at multiple timescales, first devisi…