2 citations · 5 across the 13 of their papers we have counts for
13 papers
VT-Bridge: Bridging Pretrained Foundation VLAs to VTLAs via Lightweight Residual Adaptation
Yansong Wu, Tuo Yang, Rongping Zhao +5
Vision-Tactile-Language-Action (VTLA) models have demonstrated clear advantages over Vision-Language-Action (VLA) models in contact-rich manipulation. However, developing VTLA mode…
React When You Need To: Event-Triggered Asynchronous Inference for VLA Policies
Yansong Wu, Huaqing Li, Tianding Hou +2
Vision-Language-Action (VLA) models commonly predict action chunks, limiting their ability to react to environmental changes during execution. Existing asynchronous inference metho…
Video-to-BT: Generating Reactive Behavior Trees from Human Demonstration Videos for Robotic Assembly
Xiwei Zhao, Yiwei Wang, Yansong Wu +5
Modern manufacturing demands robotic assembly systems with enhanced flexibility and reliability. However, traditional approaches often rely on programming tailored to each product…
Direction Informed Trees (DIT*): Optimal Path Planning via Direction Filter and Direction Cost Heuristic
Liding Zhang, Kejia Chen, Kuanqi Cai +7
Optimal path planning requires finding a series of feasible states from the starting point to the goal to optimize objectives. Popular path planning algorithms, such as Effort Info…
Robotic Long-Horizon Manipulation with Bayesian Non-parametric Skill Priors
Yuan Meng, Xiangtong Yao, Kejia Chen +5
Long-horizon manipulation under sparse rewards remains challenging for reinforcement learning due to delayed feedback and inefficient exploration. Existing skill-based approaches o…
SharedAssembly: A Data Collection Approach via Shared Tele-Assembly
Yansong Wu, Xiao Chen, Yu Chen +4
High-precision, tight-clearance assembly demonstrations are indispensable for training tactile-aware robotic foundation models, yet their acquisition is heavily bottlenecked by the…
Behavior Tree Generation using Large Language Models for Sequential Manipulation Planning with Human Instructions and Feedback
Jicong Ao, Yansong Wu, Fan Wu +1
In this work, we propose an LLM-based BT generation framework to leverage the strengths of both for sequential manipulation planning. To enable human-robot collaborative task plann…
TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation
Yansong Wu, Zongxie Chen, Fan Wu +6
Assembly is a crucial skill for robots in both modern manufacturing and service robotics. However, mastering transferable insertion skills that can handle a variety of high-precisi…
LLM-as-BT-Planner: Leveraging LLMs for Behavior Tree Generation in Robot Task Planning
Jicong Ao, Fan Wu, Yansong Wu +2
Robotic assembly tasks remain an open challenge due to their long horizon nature and complex part relations. Behavior trees (BTs) are increasingly used in robot task planning for t…
Tactile-Morph Skills: Energy-Based Control Meets Data-Driven Learning
Anran Zhang, Kübra Karacan, Hamid Sadeghian +3
Robotic manipulation is essential for modernizing factories and automating industrial tasks like polishing, which require advanced tactile abilities. These robots must be easily se…
Real-time Contact State Estimation in Shape Control of Deformable Linear Objects under Small Environmental Constraints
Kejia Chen, Zhenshan Bing, Yansong Wu +4
Controlling the shape of deformable linear objects using robots and constraints provided by environmental fixtures has diverse industrial applications. In order to establish robust…
Maximizing the Use of Environmental Constraints: A Pushing-Based Hybrid Position/Force Assembly Skill for Contact-Rich Tasks
Yunlei Shi, Zhaopeng Chen, Lin Cong +6
The need for contact-rich tasks is rapidly growing in modern manufacturing settings. However, few traditional robotic assembly skills consider environmental constraints during task…
Combining Learning from Demonstration with Learning by Exploration to Facilitate Contact-Rich Tasks
Yunlei Shi, Zhaopeng Chen, Yansong Wu +5
Collaborative robots are expected to be able to work alongside humans and in some cases directly replace existing human workers, thus effectively responding to rapid assembly line…