robotics
Composed Robot Policies Stop Becoming Copies
A decorrelation loss pushes diffusion-policy components toward distinct functions.
Published Updated Story ID: mp-2026-10-21-019
Summary
A decorrelation loss pushes diffusion-policy components toward distinct functions.
Compositional diffusion policies can collapse into near-identical denoisers whose average merely looks modular. Penalizing overlap between component noise predictions reduced measured double-counting and produced more meaningful specialization across tested benchmarks without reducing multitask performance.
Why it matters
A decorrelation loss pushes diffusion-policy components toward distinct functions.
Limits and context
No additional limitation was separately recorded.
Key claims
A decorrelation loss pushes diffusion-policy components toward distinct functions.
Evidence: source-2026-10-21-021
Sources
- arXiv preprint 2610.27194, original v1arXiv · primary research
Corrections
No corrections have been recorded for this story.