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

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arxiv 2407.01531 v2 pith:TVMUNXE3 submitted 2024-07-01 cs.RO cs.LG

Yixiao Wang , Yifei Zhang , Mingxiao Huo , Ran Tian , Xiang Zhang , Yichen Xie , Chenfeng Xu , Pengliang Ji
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Wei Zhan Mingyu Ding Masayoshi Tomizuka
This is my paper
classification cs.ROcs.LG
keywords policytaskslearningsparsediffusionefficientexpertsactive
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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. SDP 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 simulations and real world show that SDP 1) excels in multitask scenarios with negligible increases in active parameters, 2) prevents forgetting in continual learning of new tasks, and 3) enables efficient task transfer, offering a promising solution for advanced robotic applications. Demos and codes can be found in https://forrest-110.github.io/sparse_diffusion_policy/.

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Cited by 4 Pith papers

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  1. High-Fidelity One-Step Generative Visuomotor Policy via Recursive Correction, Frequency Consistency, and Contrastive Flow Matching

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  4. ChatVLA-2: Vision-Language-Action Model with Open-World Embodied Reasoning from Pretrained Knowledge

    cs.RO 2025-05 conditional novelty 6.0 of 10

    ChatVLA-2 uses dynamic mixture-of-experts and a two-stage training recipe to let a vision-language-action model retain pretrained reasoning while following robot instructions.

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