# DIDA: Denoised Imitation Learning based on Domain Adaptation

arXiv: https://arxiv.org/abs/2404.03382 · HTML: https://mlsanity.com/paper/2404.03382/

## Key facts

- Cited by 2 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 23 of its 41 references that are in the map.
- First posted on arXiv 4 Apr 2024.

## Placed in

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

## Builds on (most cited first, 23 of 23)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [DART: Noise Injection for Robust Imitation Learning](https://mlsanity.com/paper/1703.09327/) (2017)
- [A Divergence Minimization Perspective on Imitation Learning Methods](https://mlsanity.com/paper/1911.02256/) (2019)
- [Imitation Learning as f-Divergence Minimization](https://mlsanity.com/paper/1905.12888/) (2019)
- [Imitation Learning from Imperfect Demonstration](https://mlsanity.com/paper/1901.09387/) (2019)
- [Third-Person Imitation Learning](https://mlsanity.com/paper/1703.01703/) (2017)
- [Learning from Suboptimal Demonstration via Self-Supervised Reward Regression](https://mlsanity.com/paper/2010.11723/) (2020)
- [Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller](https://mlsanity.com/paper/1911.09676/) (2019)
- [State Alignment-based Imitation Learning](https://mlsanity.com/paper/1911.10947/) (2019)
- [HYDRA: Hybrid Robot Actions for Imitation Learning](https://mlsanity.com/paper/2306.17237/) (2023)
- [Cross-Domain Imitation Learning via Optimal Transport](https://mlsanity.com/paper/2110.03684/) (2021)
- [Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality](https://mlsanity.com/paper/2110.14754/) (2021)
- [Robust Imitation Learning from Noisy Demonstrations](https://mlsanity.com/paper/2010.10181/) (2020)
- [Robust Imitation Learning against Variations in Environment Dynamics](https://mlsanity.com/paper/2206.09314/) (2022)
- [Eliciting Compatible Demonstrations for Multi-Human Imitation Learning](https://mlsanity.com/paper/2210.08073/) (2022)
- [Multi-Task Imitation Learning for Linear Dynamical Systems](https://mlsanity.com/paper/2212.00186/) (2022)
- [Domain-Robust Visual Imitation Learning with Mutual Information Constraints](https://mlsanity.com/paper/2103.05079/) (2021)
- [Good Better Best: Self-Motivated Imitation Learning for noisy Demonstrations](https://mlsanity.com/paper/2310.15815/) (2023)
- [NeuroCERIL: Robotic Imitation Learning via Hierarchical Cause-Effect Reasoning in Programmable Attractor Neural Networks](https://mlsanity.com/paper/2211.06462/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Model-Based Approach to Imitation Learning through Multi-Step Predictions](https://mlsanity.com/paper/2504.13413/) (2025)
- [Semi-Supervised Cross-Domain Imitation Learning](https://mlsanity.com/paper/2602.10793/) (2026)

## Abstract (arXiv)

Imitating skills from low-quality datasets, such as sub-optimal demonstrations and observations with distractors, is common in real-world applications. In this work, we focus on the problem of Learning from Noisy Demonstrations (LND), where the imitator is required to learn from data with noise that often occurs during the processes of data collection or transmission. Previous IL methods improve the robustness of learned policies by injecting an adversarially learned Gaussian noise into pure expert data or utilizing additional ranking information, but they may fail in the LND setting. To alleviate the above problems, we propose Denoised Imitation learning based on Domain Adaptation (DIDA), which designs two discriminators to distinguish the noise level and expertise level of data, facilitating a feature encoder to learn task-related but domain-agnostic representations. Experiment results on MuJoCo demonstrate that DIDA can successfully handle challenging imitation tasks from demonstrations with various types of noise, outperforming most baseline methods.