Zhengtong Xu — accepted AI-conference papers

2026

Contact-Grounded Policy: Dexterous Visuotactile Policy with Generative Contact Grounding

Zhengtong Xu, Yeping Wang, Ben Abbatematteo, Jom Preechayasomboon, Sonny Chan, Nicholas Colonnese +1

RSS 2026poster

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 are highly sensitive to object geometry, frictional transitions, and slip. Recently, tactile-informed manipulation polic…

Cited by 0 Source ↗Scholar ↗

2026

UNIC: Learning Unified Multimodal Extrinsic Contact Estimation

Zhengtong Xu, Yuki Shirai

ICRA 2026poster

Contact-rich manipulation requires reliable estimation of extrinsic contacts—the interactions between a grasped object and its environment—which provide essential contextual information for planning, control, and policy learning. However, existing approaches often rely on restrictive assumptions, su…

Cited by 0 Source ↗PDF ↗Scholar ↗

2025

UniT: Data Efficient Tactile Representation With Generalization to Unseen Objects

Zhengtong Xu, Raghava Uppuluri, Xinwei Zhang, Cael Fitch, Philip G. Crandall, Wan Shou +2

RA-L 2025

UniT is an approach to tactile representation learning, using VQGAN to learn a compact latent space and serve as the tactile representation. It uses tactile images obtained from a single simple object to train the representation with generalizability. This tactile representation can be zero-shot tra

Cited by 30 Source ↗code ↗Scholar ↗

2025

VILP: Imitation Learning With Latent Video Planning

Zhengtong Xu, Qiang Qiu, Yu She

RA-L 2025

In the era of generative AI, integrating video generation models into robotics opens new possibilities for the general-purpose robot agent. This letter introduces imitation learning with latent video planning (VILP). We propose a latent video diffusion model to generate predictive robot videos that

Cited by 9 Source ↗code ↗Scholar ↗