14 citations · 38 across the 5 of their papers we have counts for
5 papers
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
Alexander Khazatsky, Karl Pertsch, Suraj Nair +98
The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. Ho…
URLB: Unsupervised Reinforcement Learning Benchmark
Michael Laskin, Denis Yarats, Hao Liu +6
Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range of complex yet specific control tasks. Yet training generalist agents that can quickly adapt to…
Hierarchical Few-Shot Imitation with Skill Transition Models
Kourosh Hakhamaneshi, Ruihan Zhao, Albert Zhan +2
A desirable property of autonomous agents is the ability to both solve long-horizon problems and generalize to unseen tasks. Recent advances in data-driven skill learning have show…
Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation
Albert Zhan, Ruihan Zhao, Lerrel Pinto +2
Recent advances in unsupervised representation learning significantly improved the sample efficiency of training Reinforcement Learning policies in simulated environments. However,…
Preventing Imitation Learning with Adversarial Policy Ensembles
Albert Zhan, Stas Tiomkin, Pieter Abbeel
Imitation learning can reproduce policies by observing experts, which poses a problem regarding policy privacy. Policies, such as human, or policies on deployed robots, can all be…