用分段筛选优化混合质量示范数据，提升机器人操作性能
Towards Effective Utilization of Mixed-Quality Demonstrations in Robotic Manipulation via Segment-Level Selection and Optimization
- 按语义分段并用对比学习选优质片段
- 仅需3个专家示范即可显著提升策略表现
- 兼容现有策略，适合真实场景数据利用
数据对机器人操作至关重要，高质量、多样化的数据能提升策略性能与适应性。但获取大量专家级数据成本高昂，导致当前数据集普遍存在质量不一致问题。为此，我们提出S2I框架，从分段层面选择并优化混合质量示范数据，同时保持与现有策略的即插即用兼容性。该框架包含三部分：示范分段（将原始数据划分为有意义片段）、片段选择（基于对比学习识别高质量片段）和轨迹优化（改进次优片段以提升策略学习效果）。在六项任务的仿真与真实环境实验中，S2I仅需3个专家示范作为参考，即可显著提升多种下游策略在混合质量数据上的表现。
原文摘要 · Abstract (English)
Data is crucial for robotic manipulation, as it underpins the development of robotic systems for complex tasks. While high-quality, diverse datasets enhance the performance and adaptability of robotic manipulation policies, collecting extensive expert-level data is resource-intensive. Consequently, many current datasets suffer from quality inconsistencies due to operator variability, highlighting the need for methods to utilize mixed-quality data effectively. To mitigate these issues, we propose "Select Segments to Imitate" (S2I), a framework that selects and optimizes mixed-quality demonstration data at the segment level, while ensuring plug-and-play compatibility with existing robotic manipulation policies. The framework has three components: demonstration segmentation dividing origin data into meaningful segments, segment selection using contrastive learning to find high-quality segments, and trajectory optimization to refine suboptimal segments for better policy learning. We evaluate S2I through comprehensive experiments in simulation and real-world environments across six tasks, demonstrating that with only 3 expert demonstrations for reference, S2I can improve the performance of various downstream policies when trained with mixed-quality demonstrations. Project website: https://tonyfang.net/s2i/.
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