6 citations · 8 across the 11 of their papers we have counts for
13 papers · 1 filter
FELT: Generating Tactile Signals from Vision for Visuo-Tactile Manipulation
Zinan Li, Yiyang Ling, Yuming Gu +8
Tactile sensing can improve performance in contact-rich robot manipulation. However, learning policies that use real tactile feedback typically requires demonstrations collected wi…
VTAP Gripper: Synergizing Fingertip Sensing and a Visuo-Tactile Active Palm for Dexterous In-Hand Manipulation
Yuhao Zhou, Sheeraz Athar, Zhixian Hu +4
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 des…
Deform360: A Massive Multi-view Visuotactile Dataset for Deformable World Models
Hongyu Li, Wanjia Fu, Xiaoyan Cong +11
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…
FlexiTac: A Low-Cost, Open-Source, Scalable Tactile Sensing Solution for Robotic Systems
Binghao Huang, Yunzhu Li
We present FlexiTac, a low-cost, open-source, and scalable piezoresistive tactile sensing solution designed for robotic end-effectors. FlexiTac is a practical "plug-in" module cons…
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…
Efficient and Reliable Teleoperation through Real-to-Sim-to-Real Shared Autonomy
Shuo Sha, Yixuan Wang, Binghao Huang +2
Fine-grained, contact-rich teleoperation remains slow, error-prone, and unreliable in real-world manipulation tasks, even for experienced operators. Shared autonomy offers a promis…
Learning Force-Regulated Robotic Manipulation with a Low-Cost Tactile-Force-Controlled Gripper
Xuhui Kang, Tongxuan Tian, Sung-Wook Lee +3
Successfully manipulating many everyday objects, such as potato chips, requires precise force regulation. Failure to modulate force can lead to task failure or irreversible damage…
VT-Refine: Learning Bimanual Assembly with Visuo-Tactile Feedback via Simulation Fine-Tuning
Binghao Huang, Jie Xu, Iretiayo Akinola +8
Humans excel at bimanual assembly tasks by adapting to rich tactile feedback -- a capability that remains difficult to replicate in robots through behavioral cloning alone, due to…
Multi-Modal Manipulation via Multi-Modal Policy Consensus
Haonan Chen, Jiaming Xu, Hongyu Chen +7
Effectively integrating diverse sensory modalities is crucial for robotic manipulation. However, the typical approach of feature concatenation is often suboptimal: dominant modalit…
Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper
Xinyue Zhu, Binghao Huang, Yunzhu Li
Handheld grippers are increasingly used to collect human demonstrations due to their ease of deployment and versatility. However, most existing designs lack tactile sensing, despit…
GenDP: 3D Semantic Fields for Category-Level Generalizable Diffusion Policy
Yixuan Wang, Guang Yin, Binghao Huang +3
Diffusion-based policies have shown remarkable capability in executing complex robotic manipulation tasks but lack explicit characterization of geometry and semantics, which often…
3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing
Binghao Huang, Yixuan Wang, Xinyi Yang +2
Tactile and visual perception are both crucial for humans to perform fine-grained interactions with their environment. Developing similar multi-modal sensing capabilities for robot…
RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulation
Hanxiao Jiang, Binghao Huang, Ruihai Wu +5
We introduce the novel task of interactive scene exploration, wherein robots autonomously explore environments and produce an action-conditioned scene graph (ACSG) that captures th…