Reproduce “DiffusionVS: A Generative Framework for Robust Visual Servoing Based on Diffusion Policy” on OpenResearch

About this paper

Hongkang Cui, Rui He, Haoyao Chen

DiffusionVS is a generative framework for visual servoing that integrates Diffusion Policy with an online training strategy to enhance robustness against noise and improve real-world generalization. The system achieves a 100% success rate in simulation and 93.3% in real-world experiments, outperforming regression-based baselines, and effectively integrates into existing visual servoing networks.

Key insights

• Generative models, specifically Diffusion Policies, are more effective for robust control tasks due to their ability to model multimodal action distributions and implicitly handle noise, leading to greater stability than deterministic regression approaches.
• Online interaction and continuous data collection are crucial for achieving robust policy learning and successful sim-to-real transfer in dynamic control tasks, as they provide diverse corrective behaviors missing in static datasets.
• The diffusion mechanism is modular and broadly applicable, capable of enhancing the performance of existing regression-based visual servoing networks when integrated as a decoder component.

Summary by alphaXiv.