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cs.RO2025
Prompt2Auto: From Motion Prompt to Automated Control via Geometry-Invariant One-Shot Gaussian Process Learning
Zewen Yang, Xiaobing Dai, Dongfa Zhang +5
Learning from demonstration allows robots to acquire complex skills from human demonstrations, but conventional approaches often require large datasets and fail to generalize acros…
cs.RO2025
UniConFlow: A Unified Constrained Flow-Matching Framework for Certified Motion Planning
Zewen Yang, Xiaobing Dai, Dian Yu +4
Generative models have become increasingly powerful tools for robot motion generation, enabling flexible and multimodal trajectory generation across various tasks. Yet, most existi…
cs.RO2025
SafeFlow: Safe Robot Motion Planning with Flow Matching via Control Barrier Functions
Xiaobing Dai, Zewen Yang, Dian Yu +4
Recent advances in generative modeling have led to promising results in robot motion planning, particularly through diffusion and flow matching (FM)-based models that capture compl…