Factored Diffusion Policies:Compositionally Generalized Robot Control with a Single Score Network
arXiv:2605.22596v1 Announce Type: new Abstract: Robotic tasks are typically specified by a tuple of factors, such as the object to be grasped, the obstacles to be avoided, the color of the target, and so on. Collecting expert demonstrations for every combination of factor values grows combinatorially. We present factored diffusion policies: a single shared diffusion network trained with per-factor null-token dropout, whose score decomposes additively across factors at inference. Under approximate conditional independence between factors given the action-observation pair, this composition appro
The increasing complexity of robot control and the combinatorial explosion of task specifications necessitate more efficient and generalizable learning architectures.
This research significantly advances the efficiency and generalization capabilities of robot control, moving closer to robots that can adapt to a wider range of unstructured tasks without extensive retraining.
Robot training methodologies can become more data-efficient, leveraging shared networks and compositional reasoning to handle diverse tasks, reducing the dependency on vast, specific datasets for every new scenario.
- · Robotics industry
- · AI research (diffusion models)
- · Automation sector
- · Logistics and manufacturing
- · Traditional robot programming (rule-based)
- · Companies relying on highly specialized robot training data
- · Inefficient robot deployment models
Robots will become more versatile and easier to deploy in complex, real-world environments.
This could accelerate the adoption of advanced robotics in sectors where combinatorial complexity was a barrier, such as healthcare and domestic services.
Increased robot generality may further blur the lines between specialized robotic systems and general-purpose autonomous agents.
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Read at arXiv cs.LG