5 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…
Function-based Parametric Co-Design Optimization of Dexterous Hands
Mohammad Amin Mirzaee, Harsh Gupta, Wenzhen Yuan
Despite advances in dexterous hand manipulation, robotic hand design is still largely decoupled from task-driven evaluation and control, limiting systematic optimization. Existing…
UMI-on-Air: Embodiment-Aware Guidance for Embodiment-Agnostic Visuomotor Policies
Harsh Gupta, Xiaofeng Guo, Huy Ha +6
We introduce UMI-on-Air, a framework for embodiment-aware deployment of embodiment-agnostic manipulation policies. Our approach leverages diverse, unconstrained human demonstration…
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…
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…