1 citations · 1 across the 10 of their papers we have counts for
14 papers
Spark-0: Scaling Spatially Aligned Tactile Pretraining for Robot Dexterous Manipulation
Tianxing Chen, Wenwei Lin, Chuqiao Lyu +19
Generalist robot policies benefit from large-scale visual pretraining but often struggle with contact-rich dexterous manipulation, where force, compliance, and incipient slip are h…
TacOT: Learning Contact-Rich Dexterous Manipulation from Human Demonstrations via Tactile-Guided Optimal Transport
Xingting Li, Yifan Han, Zijian Lin +4
Learning contact-rich dexterous manipulation from human demonstrations provides a scalable source of interaction data, yet transferring such skills to robots remains challenging du…
BiView-Touch: Learning Bimanual Tactile Representations by Cross-Hand Completion
Chenxin Liang, Youchen Lai, Chuqiao Lyu +3
Bimanual interaction produces complementary tactile views of the same physical process, yet existing tactile representation learning largely models the two hands independently or c…
DexTouch-WM: Learning Action-Conditioned Tactile World Models from Human Touch for Dexterous Robot Manipulation
Yan Qin, Yue Chen, Wenwei Lin +9
Learning predictive models of contact-rich dexterous manipulation requires dense tactile interaction, but such data are costly to scale on real robots and remain tied to embodiment…
TouchSight: Bare-Handed Tactile Prediction from Egocentric Video via Generative Visual Augmentation
Danyan Zhou, Jinxuan Lu, Jiawei Lin +3
Tactile signals provide direct contact and force measurements that are essential for understanding physical interactions and enabling dexterous robotic manipulation. However, tacti…
Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels
Xinyu Yang, Tianxing Chen, Honghao Su +38
Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction. Because failures can cause immediate physical…
TouchAnything: A Dataset and Framework for Bimanual Tactile Estimation from Egocentric Video
Jianyi Zhou, Ziteng Gao, Feiyang Hong +11
Egocentric human video data, which captures rich human-environment interactions and can be collected at scale, has become a key driver of embodied intelligence research. However, e…
FlexiCup: Wireless Multimodal Suction Cup with Dual-Zone Vision-Tactile Sensing
Junhao Gong, Shoujie Li, Kit-Wa Sou +8
Conventional suction cups lack sensing capabilities for contact-aware manipulation in unstructured environments. This paper presents FlexiCup, a multimodal suction cup with wireles…
MoiréTac: A Dual-Mode Visuotactile Sensor for Multidimensional Perception Using Moiré Pattern Amplification
Kit-Wa Sou, Junhao Gong, Shoujie Li +4
Visuotactile sensors typically employ sparse marker arrays that limit spatial resolution and lack clear analytical force-to-image relationships. To solve this problem, we present \…
UltraTac: Integrated Ultrasound-Augmented Visuotactile Sensor for Enhanced Robotic Perception
Junhao Gong, Kit-Wa Sou, Shoujie Li +5
Visuotactile sensors provide high-resolution tactile information but are incapable of perceiving the material features of objects. We present UltraTac, an integrated sensor that co…
VET: A Visual-Electronic Tactile System for Immersive Human-Machine Interaction
Cong Zhang, Yisheng Yang, Shilong Mu +4
In the pursuit of deeper immersion in human-machine interaction, achieving higher-dimensional tactile input and output on a single interface has become a key research focus. This s…
Exo-ViHa: A Cross-Platform Exoskeleton System with Visual and Haptic Feedback for Efficient Dexterous Skill Learning
Xintao Chao, Shilong Mu, Yushan Liu +4
Imitation learning has emerged as a powerful paradigm for robot skills learning. However, traditional data collection systems for dexterous manipulation face challenges, including…
AVR: Active Vision-Driven Precise Robot Manipulation with Viewpoint and Focal Length Optimization
Yushan Liu, Shilong Mu, Xintao Chao +7
Robotic manipulation in complex scenes demands precise perception of task-relevant details, yet fixed or suboptimal viewpoints often impair fine-grained perception and induce occlu…
MoDex: Planning High-Dimensional Dexterous Control via Learning Neural Internal Models
Tong Wu, Shoujie Li, Chuqiao Lyu +3
Controlling hands in high-dimensional action space has been a longstanding challenge, yet humans naturally perform dexterous tasks with ease. In this paper, we draw inspiration fro…