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Yunzhu Li

6 papers indexed

arxivcs.RO2026-07-22

FELT: Generating Tactile Signals from Vision for Visuo-Tactile Manipulation

Zinan Li, Yiyang Ling, Yuming Gu, Binghao Huang, Chenhao Liang, Sharfin Islam, et al.

The sense of touch is central to manipulation, especially when vision is occluded or ambiguous. Although combining vision and touch improves manipulation, learning robust visuo-tactile policies requires substantial tactile data. Such data remains scarcer than visual data, because…

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arxivcs.ROcs.AI2026-07-21

Agentic Real2Sim: Physics-based World Modeling with Vision-Language Agents

Guanxiong Chen, Qianjun Xia, Jiawei Peng, Heng Zhang, Bole Ma, Justin Qian, et al.

Real-to-sim conversion for robotic interaction with objects remains labor-intensive because it requires more than visual reconstruction: a streamlined real2sim process must recover scene geometries and object states, infer physical parameters, and assemble actors, objects, camera…

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arxivcs.RO2026-07-19

BoxTwin: Learning Elastoplastic Articulated Object Dynamics from Videos

Heng Zhang, Gehan Zheng, Kaifeng Zhang, Jay Song, Shivansh Patel, Sonny Hu, et al.

Digital twins enable robots to anticipate and adapt to physical interactions, but existing models struggle with elastoplastic articulated objects (EAOs) that exhibit nonlinear elasticity, plastic yielding, and damage accumulation. We present BoxTwin, an interactive digital twin f…

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arxivcs.RO2026-07-16

VTAP Gripper: Synergizing Fingertip Sensing and a Visuo-Tactile Active Palm for Dexterous In-Hand Manipulation

Yuhao Zhou, Sheeraz Athar, Zhixian Hu, Binghao Huang, Yunzhu Li, Juan Wachs, et al.

This paper presents a tactile-reactive gripper that integrates a Visuo-Tactile Active Palm (VTAP) and compliant, reconfigurable fingers equipped with tactile array sensors. The design exploits structured finger-palm synergy and multi-modal perception to achieve both robust graspi…

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arxivcs.ROcs.AIcs.CV2026-07-15

Learning Physics-Guided Residual Dynamics for Deformable Object Simulation

Shivansh Patel, Kaifeng Zhang, Sanjay Pokkali, Svetlana Lazebnik, Yunzhu Li

Simulating deformable objects is essential for a wide range of robotic manipulation applications, yet accurately predicting their dynamics remains challenging. We propose Physics-Guided Residual Dynamics (PGRD), a hybrid simulation framework that combines the advantages of physic…

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arxivcs.ROcs.CV2026-07-06

Deform360: A Massive Multi-view Visuotactile Dataset for Deformable World Models

Hongyu Li, Wanjia Fu, Xiaoyan Cong, Zekun Li, Binghao Huang, Hanxiao Jiang, et al.

Predicting object dynamics (i.e., world modeling) is a fundamental challenge for robotic manipulation, and modeling deformable objects presents a particularly difficult case due to their high-dimensional state spaces and complex material properties. While current world models app…

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