103 citations · 114 across the 6 of their papers we have counts for
6 papers
N2M: Bridging Navigation and Manipulation by Learning Pose Preference from Rollout
Kaixin Chai, Hyunjun Lee, Joseph J. Lim
Determining where to execute the manipulation policy is a fundamental challenge in mobile manipulation. Most approaches have formulated this as a geometric search problem, prioriti…
EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data
Jesse Zhang, Minho Heo, Zuxin Liu +4
Most reinforcement learning (RL) methods focus on learning optimal policies over low-level action spaces. While these methods can perform well in their training environments, they…
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…
LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers
Taewook Nam, Juyong Lee, Jesse Zhang +3
We propose a framework that leverages foundation models as teachers, guiding a reinforcement learning agent to acquire semantically meaningful behavior without human feedback. In o…
Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance
Jesse Zhang, Jiahui Zhang, Karl Pertsch +5
We propose BOSS, an approach that automatically learns to solve new long-horizon, complex, and meaningful tasks by growing a learned skill library with minimal supervision. Prior w…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…