16 papers
Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies
Pokuang Zhou, Yuhao Zhou, Quan Khanh Luu +7
Quadrupedal loco-manipulation is commonly built on visual perception and proprioception. Yet reliable contact-rich manipulation remains difficult: vision and proprioception alone c…
Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning
Shengcheng Luo, Xiyan Huang, Zhe Xu +3
Blind grasping with a dexterous hand is a crucial manipulation capability. Nevertheless, learning such tactile-only policies for real robots remains challenging due to the tactile…
PLanAR: Planning-Language-Grounded Agentic Reasoning for Robot Manipulation
Pengyuan Guo, Zhonghao Mai, Zhengtong Xu +8
Recent advances in vision-language models (VLMs) have enabled increasing progress in real-world robot manipulation. However, long-horizon manipulation in unstructured environments…
Learning Visual Feature-Based World Models via Residual Latent Action
Xinyu Zhang, Zhengtong Xu, Yutian Tao +3
World models predict future transitions from observations and actions. Existing works predominantly focus on image generation only. Visual feature-based world models, on the other…
Contact-Grounded Policy: Dexterous Visuotactile Policy with Generative Contact Grounding
Zhengtong Xu, Yeping Wang, Ben Abbatematteo +4
Contact-rich dexterous manipulation with multi-finger hands remains an open challenge in robotics because task success depends on multi-point contacts that continuously evolve and…
Tube Diffusion Policy: Reactive Visual-Tactile Policy Learning for Contact-rich Manipulation
Teng Xue, Alberto Rigo, Bingjian Huang +4
Contact-rich manipulation is central to many everyday human activities, requiring continuous adaptation to contact uncertainty and external disturbances through multi-modal percept…