9 citations · 12 across the 3 of their papers we have counts for
4 papers
ReDex: Repairing Sim-to-Real Dexterous Policies by Finger-Level Compliant Interaction
Jinzhou Li, Hadi Tabatabaee, Kelin Yu +6
Dexterous manipulation policies trained in simulation often fail to transfer to the real world because of errors in contact timing and force regulation. Yet these policies can reta…
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…
MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation
Kelin Yu, Yunhai Han, Qixian Wang +3
Tactile sensing is critical to fine-grained, contact-rich manipulation tasks, such as insertion and assembly. Prior research has shown the possibility of learning tactile-guided po…
Learning Generalizable Vision-Tactile Robotic Grasping Strategy for Deformable Objects via Transformer
Yunhai Han, Kelin Yu, Rahul Batra +6
Reliable robotic grasping, especially with deformable objects such as fruits, remains a challenging task due to underactuated contact interactions with a gripper, unknown object dy…