# Disturbance-Injected Robust Imitation Learning with Task Achievement

arXiv: https://arxiv.org/abs/2205.04195 · HTML: https://mlsanity.com/paper/2205.04195/

## Key facts

- Cited by 9 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 11 of its 32 references that are in the map.
- First posted on arXiv 9 May 2022.

## Placed in

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

## Builds on (most cited first, 11 of 11)

- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [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)
- [DART: Noise Injection for Robust Imitation Learning](https://mlsanity.com/paper/1703.09327/) (2017)
- [Imitation Learning from Imperfect Demonstration](https://mlsanity.com/paper/1901.09387/) (2019)
- [Learning from Suboptimal Demonstration via Self-Supervised Reward Regression](https://mlsanity.com/paper/2010.11723/) (2020)
- [Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments](https://mlsanity.com/paper/1910.04281/) (2019)
- [VILD: Variational Imitation Learning with Diverse-quality Demonstrations](https://mlsanity.com/paper/1909.06769/) (2019)
- [Integration of Imitation Learning using GAIL and Reinforcement Learning using Task-achievement Rewards via Probabilistic Graphical Model](https://mlsanity.com/paper/1907.02140/) (2019)
- [Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies](https://mlsanity.com/paper/2103.13623/) (2021)
- [Variational Policy Search using Sparse Gaussian Process Priors for Learning Multimodal Optimal Actions](https://mlsanity.com/paper/2106.07125/) (2021)

## Built on by (most cited first, 9 of 9)

- [Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies for Robot Manipulation](https://mlsanity.com/paper/2211.03393/) (2022)
- [Disturbance Injection under Partial Automation: Robust Imitation Learning for Long-horizon Tasks](https://mlsanity.com/paper/2303.12375/) (2023)
- [Progressive-Resolution Policy Distillation: Leveraging Coarse-Resolution Simulations for Time-Efficient Fine-Resolution Policy Learning](https://mlsanity.com/paper/2412.07477/) (2024)
- [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)
- [DAPPER: Discriminability-Aware Policy-to-Policy Preference-Based Reinforcement Learning for Query-Efficient Robot Skill Acquisition](https://mlsanity.com/paper/2505.06357/) (2025)
- [Augmentation for Learning From Demonstration with Environmental Constraints](https://mlsanity.com/paper/2210.07015/) (2022)
- [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)

Robust imitation learning using disturbance injections overcomes issues of limited variation in demonstrations. However, these methods assume demonstrations are optimal, and that policy stabilization can be learned via simple augmentations. In real-world scenarios, demonstrations are often of diverse-quality, and disturbance injection instead learns sub-optimal policies that fail to replicate desired behavior. To address this issue, this paper proposes a novel imitation learning framework that combines both policy robustification and optimal demonstration learning. Specifically, this combinatorial approach forces policy learning and disturbance injection optimization to focus on mainly learning from high task achievement demonstrations, while utilizing low achievement ones to decrease the number of samples needed. The effectiveness of the proposed method is verified through experiments using an excavation task in both simulations and a real robot, resulting in high-achieving policies that are more stable and robust to diverse-quality demonstrations. In addition, this method utilizes all of the weighted sub-optimal demonstrations without eliminating them, resulting in practical data efficiency benefits.