Sample from What You See: Visuomotor Policy Learning via Diffusion Bridge with Observation-Embedded Stochastic Differential Equation
arXiv:2512.07212v3 Announce Type: replace Abstract: Imitation learning with diffusion models has advanced robotic control by capturing the multi-modal action distributions. However, existing methods typically treat observations only as high-level conditions to the denoising network, rather than integrating them into the stochastic dynamics of the diffusion process itself. As a result, the sampling is forced to begin from random noise, weakening the coupling between perception and control and often yielding suboptimal performance. We propose BridgePolicy, a generative visuomotor policy that dir
The paper addresses a critical limitation in visuomotor policy learning by proposing a novel method to integrate observations directly into the stochastic dynamics of diffusion models, leading to more robust robotic control at a time when AI is rapidly advancing in manipulation.
This development allows for more integrated perception and control in robotics, paving the way for more capable and reliable AI-driven systems in complex real-world environments.
Existing methods which treat observations only as high-level conditions are potentially superseded by a technique that deeply integrates sensory input into the AI's decision-making process, improving performance and sample efficiency.
- · Robotics companies
- · AI research institutions
- · Automation companies
- · Companies relying on less integrated control systems
Improved performance and reliability of AI-driven robotic systems in various applications.
Accelerated deployment of autonomous robots in logistics, manufacturing, and even domestic settings.
Enhanced human-robot collaboration and the emergence of new economic sectors built around advanced robotic capabilities.
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Read at arXiv cs.AI