Leveraging Haptic Feedback to Improve Data Quality and Quantity for Deep Imitation Learning Models

TL;DR

This paper demonstrates that real-time haptic feedback during robot teleoperation enhances data collection efficiency and improves the performance of deep imitation learning models for autonomous door-opening tasks.

Contribution

It introduces a system integrating haptic feedback into teleoperation, showing improvements in data throughput and policy performance over traditional methods.

Findings

Haptic feedback increased data throughput by 6%.

Models trained with haptic feedback data performed 11% better.

Enhanced data quality led to improved autonomous robot performance.

Abstract

Learning from demonstration is a proven technique to teach robots new skills. Data quality and quantity play a critical role in the performance of models trained using data collected from human demonstrations. In this paper we enhance an existing teleoperation data collection system with real-time haptic feedback to the human demonstrators; we observe improvements in the collected data throughput and in the performance of autonomous policies using models trained with the data. Our experimental testbed was a mobile manipulator robot that opened doors with latch handles. Evaluation of teleoperated data collection on eight real conference room doors found that adding haptic feedback improved data throughput by 6%. We additionally used the collected data to train six image-based deep imitation learning models, three with haptic feedback and three without it. These models were used to implement autonomous door-opening with the same type of robot used during data collection. A policy from a imitation learning model trained with data collected while the human demonstrators received haptic feedback performed on average 11% better than its counterpart trained with data collected without haptic feedback, indicating that haptic feedback provided during data collection resulted in improved autonomous policies.