In recent years, much progress has been made in learning robotic manipulation policies that
follow natural language instructions. Such methods typically learn from corpora of robot …

Imitation learning from human demonstrations is a promising paradigm for teaching robots
manipulation skills in the real world. However, learning complex long-horizon tasks often …

We propose an approach for semantic imitation, which uses demonstrations from a source
domain, eg human videos, to accelerate reinforcement learning (RL) in a different target …

Enabling robots to learn tasks and follow instructions as easily as humans is important for
many real-world robot applications. Previous approaches have applied machine learning to …

Learning from demonstrations is a common way for users to teach robots, but it is prone to
spurious feature correlations. Recent work constructs state abstractions, ie visual …

We introduce VIOLA, an object-centric imitation learning approach to learning closed-loop
visuomotor policies for robot manipulation. Our approach constructs object-centric …

We introduce BridgeData V2, a large and diverse dataset of robotic manipulation behaviors
designed to facilitate research in scalable robot learning. BridgeData V2 contains 53,896 …

Large language models (LLMs) have been shown to exhibit a wide range of capabilities,
such as writing robot code from language commands--enabling non-experts to direct robot …

We introduce VIOLA, an object-centric imitation learning approach to learning closed-loop
visuomotor policies for robot manipulation. Our approach constructs object-centric …

Learning from human demonstrations has exhibited remarkable achievements in robot
manipulation. However, the challenge remains to develop a robot system that matches …