6 papers
EgoThumb: A Vision-Driven Supernumerary Robotic Thumb for Autonomous Grasp and Holding Assistance
Jianyu Hou, Jiawei Fu, Weikang Wan +3
Supernumerary robotic fingers augment human dexterity by enabling a single hand to perform complex "hold-and-manipulate" tasks that typically require bimanual coordination. However…
DexFLEX: Contact-Aware Foundation Controller for Command-Guided Dexterity
Weikang Wan, Zeyu Lai, Jianyu Hou +4
Dexterous robot hands can receive useful motion intent from teleoperation or learned policies, but successful execution also depends on contact decisions that these commands rarely…
AnyHand: A Large-Scale Synthetic Dataset for RGB(-D) Hand Pose Estimation
Chen Si, Yulin Liu, Bo Ai +4
We present AnyHand, a large-scale synthetic dataset designed to advance the state of the art in 3D hand pose estimation. While recent works with foundation approaches have shown th…
Scaling Cross-Embodiment World Models for Dexterous Manipulation
Zihao He, Bo Ai, Tongzhou Mu +6
Cross-embodiment learning seeks to build generalist robots that learn from and operate across diverse morphologies, but differences in kinematics and action spaces hinder data shar…
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation
Qiyue Gao, Xinyu Pi, Kevin Liu +21
Internal world models (WMs) enable agents to understand the world's state and predict transitions, serving as the basis for advanced deliberative reasoning. Recent large Vision-Lan…
Learning Adaptive Dexterous Grasping from Single Demonstrations
Liangzhi Shi, Yulin Liu, Lingqi Zeng +3
How can robots learn dexterous grasping skills efficiently and apply them adaptively based on user instructions? This work tackles two key challenges: efficient skill acquisition f…