5 papers
OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies
Kelin Yu, Haode Zhang, Harish Ravichandar +2
Visual policies learned from human videos, teleoperation, and robot demonstrations offer scalable motion priors, but often fail in contact-rich manipulation, where success signific…
HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos
Zhi Wang, Botao He, Kelin Yu +4
Human egocentric video captures rich manipulation demonstrations without any robot hardware, yet transferring these skills to robots remains challenging due to the embodiment gap b…
AFFORD2ACT: How Far Can Sparse Keypoints Go for Robot Manipulation?
Anukriti Singh, Kasra Torshizi, Khuzema Habib +3
Vision-based manipulation policies are commonly learned from rich visual observations, typically using dense visual features extracted from full resolution images. It remains uncle…
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning
Kelin Yu, Sheng Zhang, Harshit Soora +4
Recent advances have shown that video generation models can enhance robot learning by deriving effective robot actions through inverse dynamics. However, these methods heavily depe…
ControlTac: Scaling Tactile Data with Physically Controlled Tactile Image Generation
Dongyu Luo, Kelin Yu, Amir-Hossein Shahidzadeh +3
Vision-based tactile sensing is widely used in perception, reconstruction, and robotic manipulation, yet collecting large-scale tactile data remains costly due to diverse sensor-ob…