1 citations · 1 across the 10 of their papers we have counts for
14 papers
ForceWAM: Decoupling Future and Response Learning from Demo and Play Data for Contact-Rich Manipulation
Zijun Xu, Wendi Chen, Xinkai Wang +16
Expert demonstrations show how to complete contact-rich tasks, but provide limited coverage of how interaction forces respond to different actions. Task-agnostic play provides broa…
Contact Trajectory Prompting: In-Context Transfer of Contact-Rich Behaviors from a Single Demonstration
Xian Nie, Yujie Zang, Yuhang Zheng +8
In-context robot learning enables robots to acquire new behaviors from demonstrations without test-time parameter updates. However, visual and kinematic specifications alone may no…
ActiveLang: Active Open-Vocabulary 3D Mapping with Semantic-Uncertainty-Guided Exploration
Liyan Chen, Hairong Yin, Huangying Zhan +3
As robots increasingly assist humans with diverse tasks, they need both geometric and semantic understanding of their surroundings. Moreover, robots often operate in unfamiliar env…
FA-RDP: A Frequency-Adaptive Reactive Diffusion Policy for Contact-Rich Manipulation
Lifeng Zhuo, Wendi Chen, Han Xue +4
In contact-rich manipulation, action multimodality and reactivity dominate different stages of a single episode. Before contact, multiple trajectories might be equally valid, makin…
Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection
Yi Wang, Wendi Chen, Zimo Wen +8
Pretrained vision-language-action (VLA) policies provide strong language-conditioned manipulation knowledge, but they remain largely vision-driven and can struggle once manipulatio…
FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation
Chengbo Yuan, Zicheng Zhang, Mingjie Zhou +14
Despite the success of vision-based generalist robotic policies, existing tactile-based policies remain tied to fixed embodiments and sensor setups. This is because tactile signals…
VTAM: Video-Tactile-Action Models for Complex Physical Interaction Beyond VLAs
Haoran Yuan, Weigang Yi, Zhenyu Zhang +9
Video-Action Models (VAMs) have emerged as a promising framework for embodied intelligence, learning implicit world dynamics from raw video streams to produce temporally consistent…
RoboPocket: Improve Robot Policies Instantly with Your Phone
Junjie Fang, Wendi Chen, Han Xue +7
Scaling robot learning is bottlenecked not only by data volume, but also by whether the data targets the policy's failures. Existing robot-free handheld interfaces enable portable…
Rethinking Camera Choice: An Empirical Study on Fisheye Camera Properties in Robotic Manipulation
Han Xue, Nan Min, Xiaotong Liu +5
The adoption of fisheye cameras in robotic manipulation, driven by their exceptionally wide Field of View (FoV), is rapidly outpacing a systematic understanding of their downstream…
ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning
Wendi Chen, Han Xue, Yi Wang +6
Human-level contact-rich manipulation relies on the distinct roles of two key modalities: vision provides spatially rich but temporally slow global context, while force sensing cap…
SOE: Sample-Efficient Robot Policy Self-Improvement via On-Manifold Exploration
Yang Jin, Jun Lv, Han Xue +3
Intelligent agents progress by continually refining their capabilities through actively exploring environments. Yet robot policies often lack sufficient exploration capability due…
Right-Side-Out: Learning Zero-Shot Sim-to-Real Garment Reversal
Chang Yu, Siyu Ma, Wenxin Du +9
Turning garments right-side out is a challenging manipulation task: it is highly dynamic, entails rapid contact changes, and is subject to severe visual occlusion. We introduce Rig…
Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation
Han Xue, Jieji Ren, Wendi Chen +5
Humans can accomplish complex contact-rich tasks using vision and touch, with highly reactive capabilities such as fast response to external changes and adaptive control of contact…
DeformPAM: Data-Efficient Learning for Long-horizon Deformable Object Manipulation via Preference-based Action Alignment
Wendi Chen, Han Xue, Fangyuan Zhou +2
In recent years, imitation learning has made progress in the field of robotic manipulation. However, it still faces challenges when addressing complex long-horizon tasks with defor…