activity
20242026
most citedManiFeel: Benchmarking and Understanding Visuotactile Manipulation Policy Learning

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2026

ContactWorld: What Representations Matter for Vision-Tactile Latent World Models in Contact-Rich Manipulation

Zhiyuan Zhang, Pokuang Zhou, Kaidi Zhang +7

Contact-rich manipulation poses a fundamental challenge for world models: visual and tactile observations capture different aspects of physical interaction, and their utility depen…

cs.RO2026

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…

cs.RO2026★ 1 cited

Safe Learning for Contact-Rich Robot Tasks: A Survey from Classical Learning-Based Methods to Safe Foundation Models

Heng Zhang, Rui Dai, Gokhan Solak +4

Contact-rich tasks pose significant challenges for robotic systems due to inherent uncertainty, complex dynamics, and the high risk of damage during interaction. Recent advances in…

cs.RO2025★ 1 cited

ManiFeel: Benchmarking and Understanding Visuotactile Manipulation Policy Learning

Quan Khanh Luu, Pokuang Zhou, Zhengtong Xu +3

Supervised visuomotor policies have shown strong performance in robotic manipulation but often struggle in tasks with limited visual inputs, such as operations in confined spaces a…

cs.RO2024

In-Hand Singulation and Scooping Manipulation with a 5 DOF Tactile Gripper

Yuhao Zhou, Pokuang Zhou, Shaoxiong Wang +1

Manipulation tasks often require a high degree of dexterity, typically necessitating grippers with multiple degrees of freedom (DoF). While a robotic hand equipped with multiple fi…