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

Abstract

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 task-specific learning without retraining the entire model. It not only reduces the burden of active parameters but also facilitates the seamless integration and reuse of experts across various tasks. Extensive experiments on diverse tasks in both simulators and the real world show that SDP 1) excels in multitask scenarios with negligible increases in active parameters, 2) prevents forgetting in continual learning new tasks, and 3) enables efficient task transfer, offering a promising solution for advanced robotic applications. More demos and codes can be found on our https://anonymous.4open.science/w/sparse_diffusion_policy-24E7/.