mlsanityData to 7 Oct 2026

LazyDAgger: Reducing Context Switching in Interactive Imitation Learning

arXiv 2104.00053 ↗First posted 31 Mar 2021cs.RO cs.AIcited by 50

On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning

Abstract (arXiv)

Corrective interventions while a robot is learning to automate a task provide an intuitive method for a human supervisor to assist the robot and convey information about desired behavior. However, these interventions can impose significant burden on a human supervisor, as each intervention interrupts other work the human is doing, incurs latency with each context switch between supervisor and autonomous control, and requires time to perform. We present LazyDAgger, which extends the interactive imitation learning (IL) algorithm SafeDAgger to reduce context switches between supervisor and autonomous control. We find that LazyDAgger improves the performance and robustness of the learned policy during both learning and execution while limiting burden on the supervisor. Simulation experiments suggest that LazyDAgger can reduce context switches by an average of 60% over SafeDAgger on 3 continuous control tasks while maintaining state-of-the-art policy performance. In physical fabric manipulation experiments with an ABB YuMi robot, LazyDAgger reduces context switches by 60% while achieving a 60% higher success rate than SafeDAgger at execution time.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 31 of its 57 references that are in the map. First posted on arXiv 31 Mar 2021.

Builds on

31 of 57 references.

Closest ancestors · 2

Same direction, newest first

  1. 2020Human-in-the-Loop Imitation Learning using Remote Teleoperation12 Dec
  2. 2019Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor23 Sep

Other references · 29

  1. 2017Deep reinforcement learning from human preferencescited by 3,846
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  3. 2016Generative Adversarial Imitation Learningcited by 1,576
  4. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 485
  5. 2018Behavioral Cloning from Observationcited by 482
  6. 2018An Algorithmic Perspective on Imitation Learningcited by 455
  7. 2018Reward learning from human preferences and demonstrations in Ataricited by 314
  8. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 294
  9. 2019Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observationscited by 256
  10. 2018HG-DAgger: Interactive Imitation Learning with Human Expertscited by 245
  11. 2017DART: Noise Injection for Robust Imitation Learningcited by 187
  12. 2020Recovery RL: Safe Reinforcement Learning with Learned Recovery Zonescited by 156
17 more
  1. 2018EnsembleDAgger: A Bayesian Approach to Safe Imitation Learningcited by 96
  2. 2016Query-Efficient Imitation Learning for End-to-End Autonomous Drivingcited by 83
  3. 2020Safe Imitation Learning via Fast Bayesian Reward Inference from Preferencescited by 73
  4. 2019Asking Easy Questions: A User-Friendly Approach to Active Reward Learningcited by 67
  5. 2018Shared Autonomy via Deep Reinforcement Learningcited by 65
  6. 2019Learning Reward Functions by Integrating Human Demonstrations and Preferencescited by 64
  7. 2018Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behaviorcited by 40
  8. 2020VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulationcited by 40
  9. 2019AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teacherscited by 27
  10. 2020LaND: Learning to Navigate from Disengagementscited by 24
  11. 2019Scaled Autonomy: Enabling Human Operators to Control Robot Fleetscited by 16
  12. 2018Reward Learning from Narrated Demonstrationscited by 13
  13. 2018Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learningcited by 13
  14. 2018Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigationcited by 12
  15. 2019Understanding Teacher Gaze Patterns for Robot Learningcited by 12
  16. 2019On-Policy Robot Imitation Learning from a Converging Supervisorcited by 10
  17. 2020Interactive Imitation Learning in State-Spacecited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 7; 2023: 11; 2024: 8; 2025: 10; 2026: 9.

50 papers.

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  1. 2023MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrationscited by 308
  2. 2024RT-H: Action Hierarchies Using Languagecited by 204
  3. 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challengescited by 145
  4. 2021ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learningcited by 98
  5. 2023Imitating Task and Motion Planning with Visuomotor Transformerscited by 63
  6. 2021Imitation Learning: Progress, Taxonomies and Challengescited by 56
  7. 2024Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Samplingcited by 46
  8. 2024Multi-Task Interactive Robot Fleet Learning with Visual World Modelscited by 33
  9. 2022Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervisioncited by 29
  10. 2023Human-in-the-Loop Task and Motion Planning for Imitation Learningcited by 28
  11. 2023Interactive Robot Learning from Verbal Correctioncited by 28
  12. 2025Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Correctionscited by 25
38 more
  1. 2024IntervenGen: Interventional Data Generation for Robust and Data-Efficient Robot Imitation Learningcited by 24
  2. 2022Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learningcited by 22
  3. 2022Eliciting Compatible Demonstrations for Multi-Human Imitation Learningcited by 22
  4. 2022When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learningcited by 20
  5. 2022PATO: Policy Assisted TeleOperation for Scalable Robot Data Collectioncited by 19
  6. 2021Offline Preference-Based Apprenticeship Learningcited by 17
  7. 2023MEGA-DAgger: Imitation Learning with Multiple Imperfect Expertscited by 17
  8. 2023Active Reward Learning from Online Preferencescited by 12
  9. 2023A Survey of Demonstration Learningcited by 12
  10. 2025A Survey on Imitation Learning for Contact-Rich Tasks in Roboticscited by 12
  11. 2021Policy Gradient Bayesian Robust Optimization for Imitation Learningcited by 11
  12. 2024Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedbackcited by 11
  13. 2022Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learningcited by 10
  14. 2023How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvementcited by 8
  15. 2024Efficient Active Imitation Learning with Random Network Distillationcited by 7
  16. 2025Genie Centurion: Accelerating Scalable Real-World Robot Training with Human Rewind-and-Refine Guidancecited by 7
  17. 2022Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Researchcited by 6
  18. 2025Dynamic Rank Adjustment in Diffusion Policies for Efficient and Flexible Trainingcited by 6
  19. 2025Robot-Gated Interactive Imitation Learning with Adaptive Intervention Mechanismcited by 6
  20. 2024Leveraging Demonstrator-perceived Precision for Safe Interactive Imitation Learning of Clearance-limited Taskscited by 5
  21. 2023IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human Supervisorscited by 4
  22. 2026FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvementcited by 4
  23. 2021On Optimizing Interventions in Shared Autonomycited by 3
  24. 2023Learning to Explore Informative Trajectories and Samples for Embodied Perceptioncited by 3
  25. 2025Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Surveycited by 3
  26. 2025Online Imitation Learning for Manipulation via Decaying Relative Correction through Teleoperationcited by 2
  27. 2026AutoIntervene: Calibrated Intervention for Action-Chunking Imitation Learning Policiescited by 2
  28. 2026DISEIL: Demonstration Distillation for Sample-Efficient Imitation Learningcited by 1
  29. 2024DRAPER: Towards a Robust Robot Deployment and Reliable Evaluation for Quasi-Static Pick-and-Place Cloth-Shaping Neural Controllerscited by 0
  30. 2025CubeDAgger: Interactive Imitation Learning for Dynamic Systems with Efficient yet Low-risk Interactioncited by 0
  31. 2025TubeDAgger: Reducing the Number of Expert Interventions with Stochastic Reach-Tubescited by 0
  32. 2025Sample-Efficient Expert Query Control in Active Imitation Learning via Conformal Predictioncited by 0
  33. 2026Imitation Learning for Combinatorial Optimisation under Uncertaintycited by 0
  34. 2026Oracle-Guided Soft Shielding for Safe Move Prediction in Chesscited by 0
  35. 2026TAIL-Safe: Task-Agnostic Safety Monitoring for Imitation Learning Policiescited by 0
  36. 2026RouteRLT: Learning When and Which RL Specialist Should Control a Vision-Language-Action Policycited by 0
  37. 2026TANDEM: Task and Motion Planning with As-Needed Demonstrations for Efficient Vision-Language-Action Model Fine-tuningcited by 0
  38. 2026BlenDAgger: Blended Shared Control for Interactive Imitation Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026EgoSpeedUp: Transferring Human Manipulation Tempo to Robot Policies
  2. 2026Reward-DAgger: Robot-Gated Interactive Imitation Learning with General-Purpose Progress-Based Reward Models
  3. 2025Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space
  4. 2026AdaTempo: Learning Shared Relative Tempo from Demonstrations for Faster Robot Manipulation
  5. 2025RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments
  6. 2019Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning
  7. 2024Diff-DAgger: Uncertainty Estimation with Diffusion Policy for Robotic Manipulation
  8. 2026SADP: Subgoal-Aware Diffusion Policy for Long-Horizon Manipulation Learned from Foundation Model Generated Demonstrations