# Interactive Imitation Learning in Robotics: A Survey

arXiv: https://arxiv.org/abs/2211.00600 · HTML: https://mlsanity.com/paper/2211.00600/

## Key facts

- Cited by 55 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 0 of its 446 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Placed in

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

## Built on by (most cited first, 30 of 55)

- [Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment](https://mlsanity.com/paper/2211.08416/) (2022)
- [Learning Robotic Manipulation Policies from Point Clouds with Conditional Flow Matching](https://mlsanity.com/paper/2409.07343/) (2024)
- [TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction](https://mlsanity.com/paper/2405.10315/) (2024)
- [Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets](https://mlsanity.com/paper/2304.08742/) (2023)
- [Model-Based Runtime Monitoring with Interactive Imitation Learning](https://mlsanity.com/paper/2310.17552/) (2023)
- [Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations](https://mlsanity.com/paper/2408.04380/) (2024)
- [DITTO: Demonstration Imitation by Trajectory Transformation](https://mlsanity.com/paper/2403.15203/) (2024)
- [RoboCopilot: Human-in-the-loop Interactive Imitation Learning for Robot Manipulation](https://mlsanity.com/paper/2503.07771/) (2025)
- [A Survey on Imitation Learning for Contact-Rich Tasks in Robotics](https://mlsanity.com/paper/2506.13498/) (2025)
- [Human-assisted Robotic Policy Refinement via Action Preference Optimization](https://mlsanity.com/paper/2506.07127/) (2025)
- [The Art of Imitation: Learning Long-Horizon Manipulation Tasks from Few Demonstrations](https://mlsanity.com/paper/2407.13432/) (2024)
- [Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback](https://mlsanity.com/paper/2410.08852/) (2024)
- [MILE: Model-based Intervention Learning](https://mlsanity.com/paper/2502.13519/) (2025)
- [DeMaVLA: A Vision-Language-Action Foundation Model for Generalizable Deformable Manipulation](https://mlsanity.com/paper/2605.31286/) (2026)
- [FlowCorrect: Efficient Interactive Correction of Generative Flow Policies for Robotic Manipulation](https://mlsanity.com/paper/2602.22056/) (2026)
- [Whole-Body Teleoperation for Mobile Manipulation at Zero Added Cost](https://mlsanity.com/paper/2409.15095/) (2024)
- [Leveraging Demonstrator-perceived Precision for Safe Interactive Imitation Learning of Clearance-limited Tasks](https://mlsanity.com/paper/2402.13466/) (2024)
- [Interactive incremental learning of generalizable skills with local trajectory modulation](https://mlsanity.com/paper/2409.05655/) (2024)
- [RoboScape-R: Unified Reward-Observation World Models for Generalizable Robotics Training via RL](https://mlsanity.com/paper/2512.03556/) (2025)
- [Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey](https://mlsanity.com/paper/2506.00098/) (2025)
- [AutoFocus-IL: VLM-based Saliency Maps for Data-Efficient Visual Imitation Learning without Extra Human Annotations](https://mlsanity.com/paper/2511.18617/) (2025)
- [Scaling Single Human Demonstrations for Imitation Learning using Generative Foundational Models](https://mlsanity.com/paper/2602.12734/) (2026)
- [Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles](https://mlsanity.com/paper/2301.10384/) (2023)
- [Opinion-Guided Reinforcement Learning](https://mlsanity.com/paper/2405.17287/) (2024)
- [From Action Labels to Sets: Rethinking Action Supervision for Imitation Learning from Corrective Feedback](https://mlsanity.com/paper/2502.07645/) (2025)
- [OHP-RL: Online Human Preference as Guidance in Reinforcement Learning for Robot Manipulation](https://mlsanity.com/paper/2605.15971/) (2026)
- [PARTNR: Pick and place Ambiguity Resolving by Trustworthy iNteractive leaRning](https://mlsanity.com/paper/2211.08304/) (2022)
- [NAR-*ICP: Neural Execution of Classical ICP-based Pointcloud Registration Algorithms](https://mlsanity.com/paper/2410.11031/) (2024)
- [A Simple Approach to Constraint-Aware Imitation Learning with Application to Autonomous Racing](https://mlsanity.com/paper/2503.07737/) (2025)
- [Online Imitation Learning for Manipulation via Decaying Relative Correction through Teleoperation](https://mlsanity.com/paper/2503.15368/) (2025)

## Abstract (arXiv)

Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL) where human feedback is provided intermittently during robot execution allowing an online improvement of the robot's behavior. In recent years, IIL has increasingly started to carve out its own space as a promising data-driven alternative for solving complex robotic tasks. The advantages of IIL are its data-efficient, as the human feedback guides the robot directly towards an improved behavior, and its robustness, as the distribution mismatch between the teacher and learner trajectories is minimized by providing feedback directly over the learner's trajectories. Nevertheless, despite the opportunities that IIL presents, its terminology, structure, and applicability are not clear nor unified in the literature, slowing down its development and, therefore, the research of innovative formulations and discoveries. In this article, we attempt to facilitate research in IIL and lower entry barriers for new practitioners by providing a survey of the field that unifies and structures it. In addition, we aim to raise awareness of its potential, what has been accomplished and what are still open research questions. We organize the most relevant works in IIL in terms of human-robot interaction (i.e., types of feedback), interfaces (i.e., means of providing feedback), learning (i.e., models learned from feedback and function approximators), user experience (i.e., human perception about the learning process), applications, and benchmarks. Furthermore, we analyze similarities and differences between IIL and RL, providing a discussion on how the concepts offline, online, off-policy and on-policy learning should be transferred to IIL from the RL literature. We particularly focus on robotic applications in the real world and discuss their implications, limitations, and promising future areas of research.