mlsanityData to 7 Oct 2026

Cross Domain Robot Imitation with Invariant Representation

arXiv 2109.05940 ↗First posted 13 Sep 2021cs.RO cs.LGcited by 7

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

Abstract (arXiv)

Animals are able to imitate each others' behavior, despite their difference in biomechanics. In contrast, imitating the other similar robots is a much more challenging task in robotics. This problem is called cross domain imitation learning~(CDIL). In this paper, we consider CDIL on a class of similar robots. We tackle this problem by introducing an imitation learning algorithm based on invariant representation. We propose to learn invariant state and action representations, which aligns the behavior of multiple robots so that CDIL becomes possible. Compared with previous invariant representation learning methods for similar purpose, our method does not require human-labeled pairwise data for training. Instead, we use cycle-consistency and domain confusion to align the representation and increase its robustness. We test the algorithm on multiple robots in simulator and show that unseen new robot instances can be trained with existing expert demonstrations successfully. Qualitative results also demonstrate that the proposed method is able to learn similar representations for different robots with similar behaviors, which is essential for successful CDIL.

Key facts. Cited by 7 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 21 of its 30 references that are in the map. First posted on arXiv 13 Sep 2021.

Builds on

21 of 30 references.

Closest ancestors · 4

Same direction, newest first

  1. 2021Domain-Robust Visual Imitation Learning with Mutual Information Constraints8 Mar
  2. 2020ADAIL: Adaptive Adversarial Imitation Learning23 Aug
  3. 2018Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow1 Oct
  4. 2018Generative Adversarial Imitation from Observation17 Jul

Other references · 17

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  3. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
  4. 2016Generative Adversarial Imitation Learningcited by 1,576
  5. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  6. 2018MINE: Mutual Information Neural Estimationcited by 680
  7. 2014Deep Domain Confusion: Maximizing for Domain Invariancecited by 597
  8. 2020Transfer Learning in Deep Reinforcement Learning: A Surveycited by 259
  9. 2020Learning Agile Robotic Locomotion Skills by Imitating Animalscited by 229
  10. 2017Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translationcited by 167
  11. 2019AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videoscited by 105
  12. 2017Third-Person Imitation Learningcited by 103
5 more
  1. 2017Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learningcited by 84
  2. 2020Reinforcement Learning with Videos: Combining Offline Observations with Interactioncited by 80
  3. 2020Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistencycited by 43
  4. 2020Hierarchically Decoupled Imitation for Morphological Transfercited by 30
  5. 2019Improving Unsupervised Domain Adaptation with Variational Information Bottleneckcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 0; 2024: 2; 2025: 2; 2026: 1.

7 papers.

  1. 2025Humanoid Policy ~ Human Policycited by 65
  2. 2024Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space Alignmentcited by 17
  3. 2022Learn what matters: cross-domain imitation learning with task-relevant embeddingscited by 14
  4. 2022Transferable Reward Learning by Dynamics-Agnostic Discriminator Ensemblecited by 5
  5. 2024Cross-Domain Policy Transfer by Representation Alignment via Multi-Domain Behavioral Cloningcited by 1
  6. 2026Semi-Supervised Cross-Domain Imitation Learningcited by 1
  7. 2025Towards Fully Automated Decision-Making Systems for Greenhouse Control: Challenges and Opportunitiescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2020Cross-Domain Imitation Learning with a Dual Structure
  2. 2021Cross-domain Imitation from Observations
  3. 2023Domain Adaptive Imitation Learning with Visual Observation
  4. 2023Invariant Causal Imitation Learning for Generalizable Policies
  5. 2021Manipulator-Independent Representations for Visual Imitation
  6. 2022Learning and Retrieval from Prior Data for Skill-based Imitation Learning
  7. 2026Difference-Aware Retrieval Policies for Imitation Learning
  8. 2022Imitation Learning by Estimating Expertise of Demonstrators