用扩散模型拼接短轨迹，让机器人学会规划新任务。
Generative Trajectory Stitching through Diffusion Composition
- 将长轨迹拆成重叠片段，用双向扩散模型学习片段间关系。
- 在多种环境和数据条件下，性能显著优于现有方法。
- 适合需要长程规划的机器人任务，尤其训练数据有限时
长时序规划中的有效轨迹拼接是机器人决策的一大挑战。尽管扩散模型在规划中展现出潜力，但其能力局限于训练数据中类似的任务。我们提出 CompDiffuser，一种新型生成式方法，通过组合此前见过的任务中的短轨迹片段，解决新任务。核心思路是将轨迹分布分解为重叠片段，并利用单一双向扩散模型学习其条件关系，生成过程中实现片段间信息传播，确保物理上连贯的连接。我们在涵盖不同环境规模、智能体状态维度、轨迹类型及训练数据质量的基准任务上进行实验，结果表明 CompDiffuser 显著优于现有方法。
原文摘要 · Abstract (English)
Effective trajectory stitching for long-horizon planning is a significant challenge in robotic decision-making. While diffusion models have shown promise in planning, they are limited to solving tasks similar to those seen in their training data. We propose CompDiffuser, a novel generative approach that can solve new tasks by learning to compositionally stitch together shorter trajectory chunks from previously seen tasks. Our key insight is modeling the trajectory distribution by subdividing it into overlapping chunks and learning their conditional relationships through a single bidirectional diffusion model. This allows information to propagate between segments during generation, ensuring physically consistent connections. We conduct experiments on benchmark tasks of various difficulties, covering different environment sizes, agent state dimension, trajectory types, training data quality, and show that CompDiffuser significantly outperforms existing methods.
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