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

arXiv: https://arxiv.org/abs/2211.03020 · HTML: https://mlsanity.com/paper/2211.03020/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 6 Nov 2022.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 12 of 12)

- [BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning](https://mlsanity.com/paper/2202.02005/) (2022)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [Visual Imitation Made Easy](https://mlsanity.com/paper/2008.04899/) (2020)
- [Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration](https://mlsanity.com/paper/1707.02920/) (2017)
- [Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks](https://mlsanity.com/paper/1907.13098/) (2019)
- [RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer](https://mlsanity.com/paper/2011.03148/) (2020)
- [Deep Imitation Learning for Bimanual Robotic Manipulation](https://mlsanity.com/paper/2010.05134/) (2020)
- [Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor](https://mlsanity.com/paper/1910.04854/) (2019)
- [Memory-based gaze prediction in deep imitation learning for robot manipulation](https://mlsanity.com/paper/2202.04877/) (2022)
- [Practical Imitation Learning in the Real World via Task Consistency Loss](https://mlsanity.com/paper/2202.01862/) (2022)
- [ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models](https://mlsanity.com/paper/1601.03797/) (2016)

## Built on by (most cited first, 3 of 3)

- [Feasibility-aware Imitation Learning from Observations through a Hand-mounted Demonstration Interface](https://mlsanity.com/paper/2503.09018/) (2025)
- [Feasibility-aware Imitation Learning from Observation with Multimodal Feedback](https://mlsanity.com/paper/2602.15351/) (2026)
- [A Model-based Visual Contact Localization and Force Sensing System for Compliant Robotic Grippers](https://mlsanity.com/paper/2605.00307/) (2026)

## Abstract (arXiv)

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.