# Disturbance Injection under Partial Automation: Robust Imitation Learning for Long-horizon Tasks

arXiv: https://arxiv.org/abs/2303.12375 · HTML: https://mlsanity.com/paper/2303.12375/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

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

- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [HG-DAgger: Interactive Imitation Learning with Human Experts](https://mlsanity.com/paper/1810.02890/) (2018)
- [DART: Noise Injection for Robust Imitation Learning](https://mlsanity.com/paper/1703.09327/) (2017)
- [Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations](https://mlsanity.com/paper/2003.06085/) (2020)
- [Imitation Learning from Imperfect Demonstration](https://mlsanity.com/paper/1901.09387/) (2019)
- [ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning](https://mlsanity.com/paper/2109.08273/) (2021)
- [Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration](https://mlsanity.com/paper/2105.06411/) (2021)
- [Hierarchical Imitation and Reinforcement Learning](https://mlsanity.com/paper/1803.00590/) (2018)
- [Intermittent Visual Servoing: Efficiently Learning Policies Robust to Instrument Changes for High-precision Surgical Manipulation](https://mlsanity.com/paper/2011.06163/) (2020)
- [Disturbance-Injected Robust Imitation Learning with Task Achievement](https://mlsanity.com/paper/2205.04195/) (2022)
- [Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies](https://mlsanity.com/paper/2103.13623/) (2021)

## Built on by (most cited first, 4 of 4)

- [Self-Augmented Robot Trajectory: Efficient Imitation Learning via Safe Self-augmentation with Demonstrator-annotated Precision](https://mlsanity.com/paper/2509.09893/) (2025)
- [Feasibility-aware Imitation Learning from Observations through a Hand-mounted Demonstration Interface](https://mlsanity.com/paper/2503.09018/) (2025)
- [BEAC: Imitating Complex Exploration and Task-oriented Behaviors for Invisible Object Nonprehensile Manipulation](https://mlsanity.com/paper/2503.16803/) (2025)
- [Feasibility-aware Imitation Learning from Observation with Multimodal Feedback](https://mlsanity.com/paper/2602.15351/) (2026)

## Abstract (arXiv)

Partial Automation (PA) with intelligent support systems has been introduced in industrial machinery and advanced automobiles to reduce the burden of long hours of human operation. Under PA, operators perform manual operations (providing actions) and operations that switch to automatic/manual mode (mode-switching). Since PA reduces the total duration of manual operation, these two action and mode-switching operations can be replicated by imitation learning with high sample efficiency. To this end, this paper proposes Disturbance Injection under Partial Automation (DIPA) as a novel imitation learning framework. In DIPA, mode and actions (in the manual mode) are assumed to be observables in each state and are used to learn both action and mode-switching policies. The above learning is robustified by injecting disturbances into the operator's actions to optimize the disturbance's level for minimizing the covariate shift under PA. We experimentally validated the effectiveness of our method for long-horizon tasks in two simulations and a real robot environment and confirmed that our method outperformed the previous methods and reduced the demonstration burden.