Author

Haoran Guo

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#artificial intelligence Preprint Sep 2026

Touch2Robot: Robot Touch in the Human Demonstration Loop

Human demonstrations offer a scalable way to collect manipulation data, but their contacts may be unstable or infeasible when transferred to a robot hand. Collecting demonstrations directly on the target robot avoids this mismatch but substantially increases the cost of data collection. To address this trade-off, we pr...

Sheng-Cheng Luo, Xiao Cheng, Hong Ying et al. · 1 citation

Preprint Aug 2026

DexMani: Human-Derived Manipulability Guidance for Dexterous Rotation

This work introduces DexMani, a framework that transfers human demonstrations as contact-conditioned manipulability evolution and uses it to guide downstream reinforcement learning, enabling rotation skills to be acquired across robot embodiments with distinct kinematics and active-contact configurations.

Xiaoyang Chen, Shengcheng Luo, Haoran Guo et al. · 0 citations