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Rethinking Visual Embodiment Dependence in Visuomotor Policies
Hongjie Fang, Yuxuan Lu, Chenxi Wang +8
Visuomotor policies observe both the task scene and the acting embodiment, allowing embodiment-specific visual cues to influence action prediction. We study this phenomenon as visu…
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,…
Force Policy: Learning Hybrid Force-Position Control Policy under Interaction Frame for Contact-Rich Manipulation
Hongjie Fang, Shirun Tang, Mingyu Mei +9
Contact-rich manipulation demands human-like integration of perception and force feedback: vision should guide task progress, while high-frequency interaction control must stabiliz…
Learning Dexterous Manipulation with Quantized Hand State
Ying Feng, Hongjie Fang, Yinong He +5
Dexterous robotic hands enable robots to perform complex manipulations that require fine-grained control and adaptability. Achieving such manipulation is challenging because the hi…
AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons
Hongjie Fang, Chenxi Wang, Yiming Wang +11
Scaling up robotic imitation learning for real-world applications requires efficient and scalable demonstration collection methods. While teleoperation is effective, it depends on…
FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation
Zihao He, Hongjie Fang, Jingjing Chen +2
Contact-rich tasks present significant challenges for robotic manipulation policies due to the complex dynamics of contact and the need for precise control. Vision-based policies o…