4 papers
Cross-Embodiment Transfer via Behavior-Aligned Representations
Ajay Sridhar, Jensen Gao, Jonathan Yang +3
Recent progress in large-scale imitation learning for robot manipulation has been driven by leveraging datasets across a wide range of robot embodiments. However, achieving signifi…
Data Analogies Enable Efficient Cross-Embodiment Transfer
Jonathan Yang, Chelsea Finn, Dorsa Sadigh
Generalist robot policies are trained on demonstrations collected across a wide variety of robots, scenes, and viewpoints. Yet it remains unclear how to best organize and scale suc…
Invariance Co-training for Robot Visual Generalization
Jonathan Yang, Chelsea Finn, Dorsa Sadigh
Reasoning from diverse observations is a fundamental capability for generalist robot policies to operate in a wide range of environments. Despite recent advancements, many large-sc…
Bridging Perception and Action: Spatially-Grounded Mid-Level Representations for Robot Generalization
Jonathan Yang, Chuyuan Kelly Fu, Dhruv Shah +3
In this work, we investigate how spatially grounded auxiliary representations can provide both broad, high-level grounding as well as direct, actionable information to improve poli…