# Interactive Imitation Learning in Robotics: A Survey

**Authors:** Carlos Celemin, Rodrigo Pérez-Dattari, Eugenio Chisari, Giovanni Franzese, Leandro de Souza Rosa, Ravi Prakash, Zlatan Ajanović, Marta Ferraz, Abhinav Valada, Jens Kober  
**arXiv:** [2211.00600](https://arxiv.org/abs/2211.00600)  
**Date:** 2022-10-31  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2211.00600v1/

## Key points

- Interactive Imitation Learning (IIL) is a subfield of Imitation Learning (IL) in robotics that involves providing intermittent human feedback during robot execution to improve its behavior online.
- IIL has emerged as a promising data-driven alternative for solving complex robotic tasks due to its data efficiency and robustness.
- Lack of clarity and unity in terminology, structure, and applicability in the literature has slowed down its development and research progress.
- A comprehensive survey of the field has been presented to unify and structure it while raising awareness of its potential.
- Relevant works in IIL have been organized based on human-robot interaction types, interfaces for providing feedback, models learned from feedback and function approximators, user experience regarding the learning process, applications, and benchmarks.
- Similarities and differences between IIL and Reinforcement Learning (RL) have been analyzed while discussing their implications, limitations, and promising future areas of research.
- The authors focus particularly on real-world robotic applications while discussing their implications, limitations, and promising future areas of research.
- This survey aims to facilitate research in IIL by lowering entry barriers for new practitioners while highlighting what has been accomplished so far and what open research questions remain.

## Summary

Interactive Imitation Learning (IIL) is a subfield of Imitation Learning (IL) in robotics that involves providing intermittent human feedback during robot execution to improve its behavior online. In recent years, IIL has emerged as a promising data-driven alternative for solving complex robotic tasks due to its data efficiency and robustness. To address the lack of clarity and unity in terminology, structure, and applicability in the literature which has slowed down its development and research progress, this article presents a comprehensive survey of the field that unifies and structures it while raising awareness of its potential. The authors organize relevant works in IIL based on human-robot interaction types, interfaces for providing feedback, models learned from feedback and function approximators, user experience regarding the learning process, applications, and benchmarks. Additionally, they analyze similarities and differences between IIL and Reinforcement Learning (RL), discussing how concepts such as offline/online learning or off-policy/on-policy learning should be transferred from RL literature to IIL. The authors focus particularly on real-world robotic applications while discussing their implications, limitations, and promising future areas of research. This survey aims to facilitate research in IIL by lowering entry barriers for new practitioners while highlighting what has been accomplished so far and what open research questions remain. By doing so, it provides an essential reference point for researchers interested in exploring this exciting field's potential further.