2 citations · 5 across the 13 of their papers we have counts for
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MoE-DP: An MoE-Enhanced Diffusion Policy for Robust Long-Horizon Robotic Manipulation with Skill Decomposition and Failure Recovery
Baiye Cheng, Tianhai Liang, Suning Huang +5
Diffusion policies have emerged as a powerful framework for robotic visuomotor control, yet they often lack the robustness to recover from subtask failures in long-horizon, multi-s…
HDP: Triply-Hierarchical Diffusion Policy for Visuomotor Learning
Yiyang Lu, Yufeng Tian, Zhecheng Yuan +4
Visuomotor policy learning has witnessed substantial progress in robotic manipulation, with recent approaches predominantly relying on generative models to model the action distrib…
DemoSpeedup: Accelerating Visuomotor Policies via Entropy-Guided Demonstration Acceleration
Lingxiao Guo, Zhengrong Xue, Zijing Xu +1
Imitation learning has shown great promise in robotic manipulation, but the policy's execution is often unsatisfactorily slow due to commonly tardy demonstrations collected by huma…
DemoGen: Synthetic Demonstration Generation for Data-Efficient Visuomotor Policy Learning
Zhengrong Xue, Shuying Deng, Zhenyang Chen +3
Visuomotor policies have shown great promise in robotic manipulation but often require substantial amounts of human-collected data for effective performance. A key reason underlyin…