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FINGR: Learning Dexterous Hand Control for Real-World Rubik's Cube Solving
Yutong Liang, Quanquan Peng, Matthew Kim +1
Manipulating a Rubik's Cube with a single dexterous hand is a challenging test of sustained, contact-rich control: the hand must execute successive layer turns while keeping the cu…
FACT: Failure-Aware Causal Training for World-Action Models
Quanquan Peng, Yutong Liang, Rui Yan +2
Recent world-action models (WAMs) show that co-training policies with future prediction can provide physical priors for action generation. Building on the future-prediction ability…
ConTrack: Constrained Hand Motion Tracking with Adaptive Trade-off Control
Yutong Liang, Quanquan Peng, Ri-Zhao Qiu +1
Human demonstrations provide strong priors for robot manipulation, yet it is non-trivial to transfer them to execute on real robots due to the kinematic gap. In dexterous manipulat…
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…
DexterCap: An Affordable and Automated System for Capturing Dexterous Hand-Object Manipulation
Yutong Liang, Shiyi Xu, Yulong Zhang +3
Capturing fine-grained hand-object interactions is challenging due to severe self-occlusion from closely spaced fingers and the subtlety of in-hand manipulation motions. Existing o…