用压力传感器提升机器人示范数据质量，自动采集更优策略。
Instrumentation for Better Demonstrations: A Case Study
- 在挤压瓶上加压力传感器，实现自动化数据采集
- 基于自动示范训练的模型在78%情况下优于人类示范
- 适合想提升机器人学习数据质量的研究者
从示范中学习是机器人操作的有效范式，但其效果依赖于数据的数量与质量。本文通过案例研究发现，集成传感器（即仪器化）可提升示范质量并实现自动化数据采集。我们为挤压瓶加装压力传感器，利用PI控制器实现自动化数据采集。基于自动化示范训练的基于Transformer的策略，在78%的情况下表现优于人类示范数据训练的模型。结果表明，仪器化不仅促进可扩展的数据采集，还能生成性能更优的策略，对发展通用机器人智能具有重要潜力。
原文摘要 · Abstract (English)
Learning from demonstrations is a powerful paradigm for robot manipulation, but its effectiveness hinges on both the quantity and quality of the collected data. In this work, we present a case study of how instrumentation, i.e. integration of sensors, can improve the quality of demonstrations and automate data collection. We instrument a squeeze bottle with a pressure sensor to learn a liquid dispensing task, enabling automated data collection via a PI controller. Transformer-based policies trained on automated demonstrations outperform those trained on human data in 78% of cases. Our findings indicate that instrumentation not only facilitates scalable data collection but also leads to better-performing policies, highlighting its potential in the pursuit of generalist robotic agents.
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