通过状态级轨迹拼接实现鲁棒的离线模仿学习
Robust Offline Imitation Learning Through State-level Trajectory Stitching
AI总结：
针对传统模仿学习依赖高质量专家数据、存在协变量偏移的问题，提出基于状态搜索的轨迹拼接框架，利用混合质量离线数据生成多样训练轨迹，提升机器人策略的泛化性与性能。
摘要：
模仿学习（IL）已被证实能有效帮助机器人通过专家演示获取视觉运动技能。但传统IL方法受限于对高质量且通常稀缺的专家数据的依赖，还存在协变量偏移问题。为应对这些挑战，近期离线IL的进展已将次优的无标注数据集纳入训练。本文提出一种新方法，通过利用任务相关轨迹片段和丰富的环境动力学，从混合质量的离线数据集中增强策略学习。具体而言，我们引入一个基于状态的搜索框架，拼接来自不完美演示的状态-动作对，生成更多样且信息量更大的训练轨迹。在标准IL基准和真实世界机器人任务上的实验结果表明，我们提出的方法显著提升了泛化性和性能。
英文摘要：
Imitation learning (IL) has proven effective for enabling robots to acquire visuomotor skills through expert demonstrations. However, traditional IL methods are limited by their reliance on high-quality, often scarce, expert data, and suffer from covariate shift. To address these challenges, recent advances in offline IL have incorporated suboptimal, unlabeled datasets into the training. In this paper, we propose a novel approach to enhance policy learning from mixed-quality offline datasets by leveraging task-relevant trajectory fragments and rich environmental dynamics. Specifically, we introduce a state-based search framework that stitches state-action pairs from imperfect demonstrations, generating more diverse and informative training trajectories. Experimental results on standard IL benchmarks and real-world robotic tasks showcase that our proposed method significantly improves both generalization and performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
