6 papers
LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisition
Harsh Gupta, Guanya Shi, Wenzhen Yuan
The most widely-adopted robot learning pipelines today learn skills from robot demonstrations or structured human data, which are expensive to collect and tied to specific embodime…
PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation
Yuanzhe Liu, Jingyuan Zhu, Yuchen Mo +9
Recent advancements in vision-language-action (VLA) models have shown promise in robotic manipulation, yet they continue to struggle with long-horizon, multi-step tasks. Existing m…
Grasp to Act: Dexterous Grasping for Tool Use in Dynamic Settings
Harsh Gupta, Mohammad Amin Mirzaee, Wenzhen Yuan
Achieving robust grasping with dexterous hands remains challenging, especially when manipulation involves dynamic forces such as impacts, torques, and continuous resistance--situat…
DoorBot: Closed-Loop Task Planning and Manipulation for Door Opening in the Wild with Haptic Feedback
Zhi Wang, Yuchen Mo, Shengmiao Jin +1
Robots operating in unstructured environments face significant challenges when interacting with everyday objects like doors. They particularly struggle to generalize across diverse…
Sensor-Invariant Tactile Representation
Harsh Gupta, Yuchen Mo, Shengmiao Jin +1
High-resolution tactile sensors have become critical for embodied perception and robotic manipulation. However, a key challenge in the field is the lack of transferability between…
Learning to Double Guess: An Active Perception Approach for Estimating the Center of Mass of Arbitrary Objects
Shengmiao Jin, Yuchen Mo, Wenzhen Yuan
Manipulating arbitrary objects in unstructured environments is a significant challenge in robotics, primarily due to difficulties in determining an object's center of mass. This pa…