标题： 状态覆盖轨迹拼接用于扩散规划器 显示英文标题

标题： State-Covering Trajectory Stitching for Diffusion Planners

摘要： 基于扩散的生成模型正在成为强化学习（RL）中长期规划的强大工具，特别是在离线数据集的情况下。然而，它们的表现从根本上受到训练数据质量和多样性的限制。这通常限制了它们在超出训练分布的任务或更长规划范围上的泛化能力。为了解决这一挑战，我们提出了State-Covering Trajectory Stitching（SCoTS），这是一种新颖的无奖励轨迹增强方法，它逐步将短轨迹片段缝合在一起，系统地生成多样化和扩展的轨迹。SCoTS首先学习一种保留时间距离的潜在表示，以捕捉环境的基本时间结构，然后通过方向探索和新颖性迭代缝合轨迹片段，有效地覆盖和扩展这个潜在空间。我们证明了SCoTS显著提高了扩散规划器在需要缝合和长时程推理的离线目标条件基准上的表现和泛化能力。此外，由SCoTS生成的增强轨迹显著提升了广泛使用的离线目标条件RL算法在各种环境中的性能。 显示英文摘要

摘要： Diffusion-based generative models are emerging as powerful tools for long-horizon planning in reinforcement learning (RL), particularly with offline datasets. However, their performance is fundamentally limited by the quality and diversity of training data. This often restricts their generalization to tasks outside their training distribution or longer planning horizons. To overcome this challenge, we propose State-Covering Trajectory Stitching (SCoTS), a novel reward-free trajectory augmentation method that incrementally stitches together short trajectory segments, systematically generating diverse and extended trajectories. SCoTS first learns a temporal distance-preserving latent representation that captures the underlying temporal structure of the environment, then iteratively stitches trajectory segments guided by directional exploration and novelty to effectively cover and expand this latent space. We demonstrate that SCoTS significantly improves the performance and generalization capabilities of diffusion planners on offline goal-conditioned benchmarks requiring stitching and long-horizon reasoning. Furthermore, augmented trajectories generated by SCoTS significantly improve the performance of widely used offline goal-conditioned RL algorithms across diverse environments.