Reproduce “Cold Diffusion on the Replay Buffer: Learning to Plan from Known Good States” on OpenResearch

About this paper

Zidan Wang, Takeru Oba, Takuma Yoneda, Rui Shen, Matthew Walter, Bradly C. Stadie

Cold Diffusion on the Replay Buffer (CDRB) enhances robot trajectory planning by leveraging a replay buffer of expert demonstrations within a cold diffusion framework. It consistently generates collision-free and physically feasible paths in challenging environments, achieving significantly higher success rates than Gaussian diffusion models like Diffuser.

Key insights

• Leveraging a replay buffer of expert demonstrations as an explicit source of "known good states" implicitly encodes environmental constraints into the diffusion process, making the model inherently aware of obstacles.
• The use of "cold diffusion" enables a custom degradation process that maps trajectory points to nearby replay buffer points, guiding trajectory generation through a manifold of feasible states.
• This approach enhances safety and generalization, making robot planning more robust in complex, constrained environments without requiring explicit definitions of obstacles or cost functions.

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