Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning

Understand

The increasing complexity of tasks in robotics demands efficient strategies for multitask and continual learning.

• Traditional models typically rely on a universal policy for all tasks, facing challenges such as high computational costs and catastrophic forgetting when learning new tasks.
• To address these issues, we introduce a sparse, reusable, and flexible policy, Sparse Diffusion Policy (SDP).
• By adopting Mixture of Experts (MoE) within a transformer-based diffusion policy, SDP selectively activates experts and skills, enabling efficient and task-specific learning without retraining the entire model.