8 papers · 1 filter
ReForce: Learning Force-aware Retargeting for Dexterous Manipulation
Yuhang Wu, Lingqi Zeng, Changwei Jing +2
Human demonstrations offer a scalable data source for dexterous manipulation, but transferring them to robot actions remains challenging due to the embodiment gap. Today's retarget…
Cross-Hand Latent Representation for Vision-Language-Action Models
Guangqi Jiang, Yutong Liang, Jianglong Ye +6
Dexterous manipulation is essential for real-world robot autonomy, mirroring the central role of human hand coordination in daily activity. Humans rely on rich multimodal perceptio…
Contact-Aware Neural Dynamics
Changwei Jing, Jai Krishna Bandi, Jianglong Ye +4
High-fidelity physics simulation is essential for scalable robotic learning, but the sim-to-real gap persists, especially for tasks involving complex, dynamic, and discontinuous in…
From Power to Precision: Learning Fine-grained Dexterity for Multi-fingered Robotic Hands
Jianglong Ye, Lai Wei, Guangqi Jiang +3
Human grasps can be roughly categorized into two types: power grasps and precision grasps. Precision grasping enables tool use and is believed to have influenced human evolution. T…
Dex1B: Learning with 1B Demonstrations for Dexterous Manipulation
Jianglong Ye, Keyi Wang, Chengjing Yuan +6
Generating large-scale demonstrations for dexterous hand manipulation remains challenging, and several approaches have been proposed in recent years to address this. Among them, ge…
Co-Design of Soft Gripper with Neural Physics
Sha Yi, Xueqian Bai, Adabhav Singh +3
For robot manipulation, both the controller and end-effector design are crucial. Soft grippers are generalizable by deforming to different geometries, but designing such a gripper…