Instrumentation for Better Demonstrations: A Case Study

2025-04-25

AI Summary

This study addresses the dual challenges of low-quality demonstrations and inefficient data collection in robotic imitation learning. We propose an instrumented demonstration framework, exemplified on a liquid dispensing task: a pressure sensor is embedded within a squeeze bottle, and a PI closed-loop controller enables high-precision, automated demonstration generation. Compared to conventional human demonstrations, our approach improves policy performance in 78% of test scenarios. Experiments show that instrumented collection not only substantially increases the volume of high-fidelity demonstration data but also enables Transformer-based imitation policies trained on automated demonstrations to outperform those trained on human demonstrations—on average—across evaluation metrics. To our knowledge, this is the first empirical validation that sensor-augmented automated demonstration collection simultaneously enhances data quality, scalability, and downstream policy generalization. Our work establishes a scalable, sensor-instrumented data infrastructure paradigm for developing general-purpose robotic agents.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Improving demonstration quality via sensor integration
• Automating data collection for robot manipulation tasks
• Enhancing policy performance through instrumented demonstrations

Innovation

• Integration of sensors improves demonstration quality
• Pressure sensor enables automated data collection
• Transformer policies outperform human-trained ones