# Investigating the Effects of Robot Engagement Communication on Learning from Demonstration

arXiv: https://arxiv.org/abs/2005.01020 · HTML: https://mlsanity.com/paper/2005.01020/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 6 of its 73 references that are in the map.
- First posted on arXiv 3 May 2020.

## Placed in

- Offline RL and imitation learning from data / [Robot learning from demonstration](https://mlsanity.com/offlinerl/robot-learning-from-demonstration/)

## Builds on (most cited first, 6 of 6)

- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Interactive Policy Learning through Confidence-Based Autonomy](https://mlsanity.com/paper/1401.3439/) (2014)
- [Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations](https://mlsanity.com/paper/1610.00850/) (2016)
- [Adversarial Imitation Learning from Incomplete Demonstrations](https://mlsanity.com/paper/1905.12310/) (2019)
- [Expressing Robot Incapability](https://mlsanity.com/paper/1810.08167/) (2018)
- [Estimating Emotional Intensity from Body Poses for Human-Robot Interaction](https://mlsanity.com/paper/1904.09435/) (2019)

## Abstract (arXiv)

Robot Learning from Demonstration (RLfD) is a technique for robots to derive policies from instructors' examples. Although the reciprocal effects of student engagement on teacher behavior are widely recognized in the educational community, it is unclear whether the same phenomenon holds true for RLfD. To fill this gap, we first design three types of robot engagement behavior (attention, imitation, and a hybrid of the two) based on the learning literature. We then conduct, in a simulation environment, a within-subject user study to investigate the impact of different robot engagement cues on humans compared to a "without-engagement" condition. Results suggest that engagement communication significantly changes the human's estimation of the robots' capability and significantly raises their expectation towards the learning outcomes, even though we do not run actual learning algorithms in the experiments. Moreover, imitation behavior affects humans more than attention does in all metrics, while their combination has the most profound influences on humans. We also find that communicating engagement via imitation or the combined behavior significantly improve humans' perception towards the quality of demonstrations, even if all demonstrations are of the same quality.