465 citations · 4.1k across the 97 of their papers we have counts for
27 papers · 1 filter
Coarse-to-fine Q-attention with Tree Expansion
Stephen James, Pieter Abbeel
Coarse-to-fine Q-attention enables sample-efficient robot manipulation by discretizing the translation space in a coarse-to-fine manner, where the resolution gradually increases at…
Coarse-to-Fine Q-attention with Learned Path Ranking
Stephen James, Pieter Abbeel
We propose Learned Path Ranking (LPR), a method that accepts an end-effector goal pose, and learns to rank a set of goal-reaching paths generated from an array of path generating m…
Bingham Policy Parameterization for 3D Rotations in Reinforcement Learning
Stephen James, Pieter Abbeel
We propose a new policy parameterization for representing 3D rotations during reinforcement learning. Today in the continuous control reinforcement learning literature, many stocha…
Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning
Wenlong Huang, Igor Mordatch, Pieter Abbeel +1
Dexterous manipulation of arbitrary objects, a fundamental daily task for humans, has been a grand challenge for autonomous robotic systems. Although data-driven approaches using r…
Playful Interactions for Representation Learning
Sarah Young, Jyothish Pari, Pieter Abbeel +1
One of the key challenges in visual imitation learning is collecting large amounts of expert demonstrations for a given task. While methods for collecting human demonstrations are…
Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble
Seunghyun Lee, Younggyo Seo, Kimin Lee +2
Recent advance in deep offline reinforcement learning (RL) has made it possible to train strong robotic agents from offline datasets. However, depending on the quality of the train…