23 papers
FA-RDP: A Frequency-Adaptive Reactive Diffusion Policy for Contact-Rich Manipulation
Lifeng Zhuo, Wendi Chen, Han Xue +4
The paper introduces FA-RDP, a diffusion‑based policy that adapts its inference frequency during contact‑rich manipulation, using a multi‑frequency visual‑force transformer and a m…
Asynchronous Multimodal Diffusion Policy Composition via Latency-Aware Guidance Fusion
Zihao He, Hongjie Fang, Shirun Tang +2
Diffusion policies have shown strong potential for robotic imitation learning, and recent extensions incorporate additional modalities to improve manipulation performance. However,…
Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection
Yi Wang, Wendi Chen, Zimo Wen +8
The paper introduces LIFT, a post‑training method that adds reactive force feedback to pretrained vision‑language‑action policies, enabling them to handle contact‑rich manipulation…
AnyDexRT: Calibration-Free Dexterous Hand Retargeting with Few-Shot Human Guidance
Chenxi Wang, Ying Feng, Hongjie Fang +4
Teleoperation is a key interface for controlling dexterous robotic hands and collecting demonstrations for imitation learning. Its effectiveness largely depends on kinematic retarg…
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…
Scaling by Diversified Experience for Vision-Language-Action Models
Leiyu Wang, Zhaofengnian Wang, Xueqi Li +3
Vision-Language-Action models face significant challenges in real-world deployment due to the entanglement of high-level reasoning with low-level control, and the instability of po…