用分解扩散模型提升机器人多任务学习的适应性与泛化能力
Flexible Multitask Learning with Factorized Diffusion Policy
- 将复杂动作分布分解为多个专用扩散模型，分别捕捉不同行为模式
- 在仿真和真实机器人场景中均超越主流模块化与单一模型基准
- 支持灵活增删组件，实现新任务快速适配且避免灾难性遗忘
多任务学习因机器人动作分布高度多元且复杂而面临挑战。现有单体模型常无法充分拟合动作分布，适应性差。本文提出一种新型模块化扩散策略框架，将复杂动作分布分解为多个专门化的扩散模型，各自捕获行为空间中的特定子模式，从而构建更有效的整体策略。该模块化结构可通过添加或微调组件灵活适应新任务，天然缓解灾难性遗忘。在仿真与真实机器人操作环境中，实验表明本方法始终优于强基线的模块化与单体模型。
原文摘要 · Abstract (English)
Multitask learning poses significant challenges due to the highly multimodal and diverse nature of robot action distributions. However, effectively fitting policies to these complex task distributions is often difficult, and existing monolithic models often underfit the action distribution and lack the flexibility required for efficient adaptation. We introduce a novel modular diffusion policy framework that factorizes complex action distributions into a composition of specialized diffusion models, each capturing a distinct sub-mode of the behavior space for a more effective overall policy. In addition, this modular structure enables flexible policy adaptation to new tasks by adding or fine-tuning components, which inherently mitigates catastrophic forgetting. Empirically, across both simulation and real-world robotic manipulation settings, we illustrate how our method consistently outperforms strong modular and monolithic baselines.
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