# Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies

arXiv: https://arxiv.org/abs/2103.13623 · HTML: https://mlsanity.com/paper/2103.13623/

## Key facts

- Cited by 6 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 5 of its 22 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Placed in

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

## Builds on (most cited first, 5 of 5)

- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [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)
- [Kernelized Movement Primitives](https://mlsanity.com/paper/1708.08638/) (2017)

## Built on by (most cited first, 6 of 6)

- [Coherent Soft Imitation Learning](https://mlsanity.com/paper/2305.16498/) (2023)
- [Disturbance-Injected Robust Imitation Learning with Task Achievement](https://mlsanity.com/paper/2205.04195/) (2022)
- [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)
- [Robotic Learning the Sequence of Packing Irregular Objects from Human Demonstrations](https://mlsanity.com/paper/2210.01645/) (2022)
- [BEAC: Imitating Complex Exploration and Task-oriented Behaviors for Invisible Object Nonprehensile Manipulation](https://mlsanity.com/paper/2503.16803/) (2025)

## Abstract (arXiv)

Scenarios requiring humans to choose from multiple seemingly optimal actions are commonplace, however standard imitation learning often fails to capture this behavior. Instead, an over-reliance on replicating expert actions induces inflexible and unstable policies, leading to poor generalizability in an application. To address the problem, this paper presents the first imitation learning framework that incorporates Bayesian variational inference for learning flexible non-parametric multi-action policies, while simultaneously robustifying the policies against sources of error, by introducing and optimizing disturbances to create a richer demonstration dataset. This combinatorial approach forces the policy to adapt to challenging situations, enabling stable multi-action policies to be learned efficiently. The effectiveness of our proposed method is evaluated through simulations and real-robot experiments for a table-sweep task using the UR3 6-DOF robotic arm. Results show that, through improved flexibility and robustness, the learning performance and control safety are better than comparison methods.