mlsanityData to 7 Oct 2026

Learn what matters: cross-domain imitation learning with task-relevant embeddings

arXiv 2209.12093 ↗First posted 24 Sep 2022cs.AIcited by 14

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

We study how an autonomous agent learns to perform a task from demonstrations in a different domain, such as a different environment or different agent. Such cross-domain imitation learning is required to, for example, train an artificial agent from demonstrations of a human expert. We propose a scalable framework that enables cross-domain imitation learning without access to additional demonstrations or further domain knowledge. We jointly train the learner agent's policy and learn a mapping between the learner and expert domains with adversarial training. We effect this by using a mutual information criterion to find an embedding of the expert's state space that contains task-relevant information and is invariant to domain specifics. This step significantly simplifies estimating the mapping between the learner and expert domains and hence facilitates end-to-end learning. We demonstrate successful transfer of policies between considerably different domains, without extra supervision such as additional demonstrations, and in situations where other methods fail.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 30 of its 59 references that are in the map. First posted on arXiv 24 Sep 2022.

Builds on

30 of 59 references.

Closest ancestors · 11

Same direction, newest first

  1. 2022Robust Learning from Observation with Model Misspecification12 Feb
  2. 2021Cross-Domain Imitation Learning via Optimal Transport7 Oct
  3. 2021Cross Domain Robot Imitation with Invariant Representation13 Sep
  4. 2021What Matters for Adversarial Imitation Learning?1 Jun
  5. 2021Cross-domain Imitation from Observations20 May
  6. 2021Domain-Robust Visual Imitation Learning with Mutual Information Constraints8 Mar
  7. 2020The MAGICAL Benchmark for Robust Imitation1 Nov
  8. 2020State-only Imitation with Transition Dynamics Mismatch27 Feb
  9. 2019State Alignment-based Imitation Learning21 Nov
  10. 2018Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning9 Sep
  11. 2018Generative Adversarial Imitation from Observation17 Jul

Other references · 19

  1. 2016OpenAI Gymcited by 1,684
  2. 2016Generative Adversarial Imitation Learningcited by 1,576
  3. 2019Solving Rubik's Cube with a Robot Handcited by 699
  4. 2018MINE: Mutual Information Neural Estimationcited by 680
  5. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 485
  6. 2017Time-Contrastive Networks: Self-Supervised Learning from Videocited by 453
  7. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 294
  8. 2020Decoupling Representation Learning from Reinforcement Learningcited by 263
  9. 2019Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Modelcited by 228
  10. 2019Unsupervised State Representation Learning in Ataricited by 144
  11. 2017Third-Person Imitation Learningcited by 103
  12. 2021XIRL: Cross-embodiment Inverse Reinforcement Learningcited by 101
7 more
  1. 2020State-Only Imitation Learning for Dexterous Manipulationcited by 90
  2. 2017Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learningcited by 84
  3. 2020Reinforcement Learning with Videos: Combining Offline Observations with Interactioncited by 80
  4. 2020Deep Reinforcement and InfoMax Learningcited by 79
  5. 2021Which Mutual-Information Representation Learning Objectives are Sufficient for Control?cited by 28
  6. 2022Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviorscited by 27
  7. 2021Skeletal Feature Compensation for Imitation Learning with Embodiment Mismatchcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 5; 2025: 3; 2026: 1.

14 papers.

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  1. 2025Humanoid Policy ~ Human Policycited by 65
  2. 2023Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learningcited by 27
  3. 2024A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agentscited by 16
  4. 2023CEIL: Generalized Contextual Imitation Learningcited by 12
  5. 2023CLUE: Calibrated Latent Guidance for Offline Reinforcement Learningcited by 11
  6. 2023Domain Adaptive Imitation Learning with Visual Observationcited by 11
  7. 2024xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory Editingcited by 9
  8. 2023Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrationscited by 8
  9. 2025Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metricscited by 7
  10. 2024SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoringcited by 1
  11. 2024Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Reviewcited by 1
  12. 2024Cross-Domain Policy Transfer by Representation Alignment via Multi-Domain Behavioral Cloningcited by 1
2 more
  1. 2026Semi-Supervised Cross-Domain Imitation Learningcited by 1
  2. 2025Learning from Observation: A Survey of Recent Advancescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2020ADAIL: Adaptive Adversarial Imitation Learning
  2. 2020Cross-Domain Imitation Learning with a Dual Structure
  3. 2022Learning from Guided Play: Improving Exploration for Adversarial Imitation Learning with Simple Auxiliary Tasks
  4. 2019Domain Adaptive Imitation Learning
  5. 2025Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advances
  6. 2022Latent Policies for Adversarial Imitation Learning
  7. 2026Latent Wasserstein Adversarial Imitation Learning
  8. 2023Adversarial Imitation Learning from Visual Observations using Latent Information