mlsanityData to 7 Oct 2026

Easy-IIL: Reducing Human Operational Burden in Interactive Imitation Learning via Assistant Experts

arXiv 2603.12769 ↗First posted 13 Mar 2026cs.ROcited by 0

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

Abstract (arXiv)

Interactive Imitation Learning (IIL) typically relies on extensive human involvement for both offline demonstration and online interaction. Prior work primarily focuses on reducing human effort in passive monitoring rather than active operation. Interestingly, structured model-based imitation approaches achieve comparable performance with significantly fewer demonstrations than end-to-end imitation learning policies in the low-data regime. However, these methods are typically surpassed by end-to-end policies as the data increases. Leveraging this insight, we propose Easy-IIL, a framework that utilizes off-the-shelf model-based imitation methods as an assistant expert to replace active human operation for the majority of data collection. The human expert only provides a single demonstration to initialize the assistant expert and intervenes in critical states where the task is approaching failure. Furthermore, Easy-IIL can maintain IIL performance by preserving both offline and online data quality. Extensive simulation and real-world experiments demonstrate that Easy-IIL significantly reduces human operational burden while maintaining performance comparable to mainstream IIL baselines. User studies further confirm that Easy-IIL reduces subjective workload on the human expert. Project page: https://sites.google.com/view/easy-iil

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 35 of its 45 references that are in the map. First posted on arXiv 13 Mar 2026.

Builds on

35 of 45 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025MILE: Model-based Intervention Learning19 Feb
  2. 2023RLIF: Interactive Imitation Learning as Reinforcement Learning21 Nov
  3. 2021ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning17 Sep
  4. 2018HG-DAgger: Interactive Imitation Learning with Human Experts5 Oct
  5. 2018EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning22 Jul

Other references · 30

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusioncited by 2,730
  3. 2023Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardwarecited by 1,490
  4. 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,313
  5. 2024Grounded SAM: Assembling Open-World Models for Diverse Visual Taskscited by 914
  6. 2015End-to-End Training of Deep Visuomotor Policiescited by 847
  7. 2019RLBench: The Robot Learning Benchmark & Learning Environmentcited by 669
  8. 2023LightGlue: Local Feature Matching at Light Speedcited by 583
  9. 2023MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrationscited by 308
  10. 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 286
  11. 2023GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulatorscited by 180
  12. 2020Human-in-the-Loop Imitation Learning using Remote Teleoperationcited by 103
18 more
  1. 2022Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deploymentcited by 101
  2. 2025DemoGen: Synthetic Demonstration Generation for Data-Efficient Visuomotor Policy Learningcited by 64
  3. 2024SkillMimicGen: Automated Demonstration Generation for Efficient Skill Learning and Deploymentcited by 48
  4. 2024DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Modelscited by 43
  5. 2025Real2Render2Real: Scaling Robot Data Without Dynamics Simulation or Robot Hardwarecited by 41
  6. 2023Model-Based Runtime Monitoring with Interactive Imitation Learningcited by 37
  7. 2024DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Democited by 34
  8. 2022Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervisioncited by 29
  9. 2024Diff-DAgger: Uncertainty Estimation with Diffusion Policy for Robotic Manipulationcited by 29
  10. 2024DITTO: Demonstration Imitation by Trajectory Transformationcited by 28
  11. 2024CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulationcited by 25
  12. 2021Correct Me if I am Wrong: Interactive Learning for Robotic Manipulationcited by 24
  13. 2021Learning Multi-Stage Tasks with One Demonstration via Self-Replaycited by 21
  14. 2023One-Shot Imitation Learning: A Pose Estimation Perspectivecited by 21
  15. 2025MimicFunc: Imitating Tool Manipulation from a Single Human Video via Functional Correspondencecited by 11
  16. 2025FUNCTO: Function-Centric One-Shot Imitation Learning for Tool Manipulationcited by 8
  17. 2025One-Shot Dual-Arm Imitation Learningcited by 7
  18. 2024Visual Imitation Learning of Task-Oriented Object Grasping and Rearrangementcited by 3

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  1. 2022Interactive Imitation Learning in Robotics: A Survey
  2. 2024VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections
  3. 2021Imitation Learning: Progress, Taxonomies and Challenges
  4. 2025Using Non-Expert Data to Robustify Imitation Learning via Offline Reinforcement Learning
  5. 2024A Survey of Imitation Learning Methods, Environments and Metrics
  6. 2024Interactive and Hybrid Imitation Learning: Provably Beating Behavior Cloning
  7. 2025Robot-Gated Interactive Imitation Learning with Adaptive Intervention Mechanism
  8. 2025Imitation Learning in the Deep Learning Era: A Novel Taxonomy and Recent Advances