# Social Navigation with Human Empowerment driven Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2003.08158 · HTML: https://mlsanity.com/paper/2003.08158/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches](https://mlsanity.com/paper/2512.00049/) (2025)
- [In Search of a Lost Metric: Human Empowerment as a Pillar of Socially Conscious Navigation](https://mlsanity.com/paper/2501.01539/) (2025)
- [Reward Shaping with Subgoals for Social Navigation](https://mlsanity.com/paper/2104.06410/) (2021)
- [Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer Empowerment](https://mlsanity.com/paper/2203.03355/) (2022)

## Abstract (arXiv)

Mobile robot navigation has seen extensive research in the last decades. The aspect of collaboration with robots and humans sharing workspaces will become increasingly important in the future. Therefore, the next generation of mobile robots needs to be socially-compliant to be accepted by their human collaborators. However, a formal definition of compliance is not straightforward. On the other hand, empowerment has been used by artificial agents to learn complicated and generalized actions and also has been shown to be a good model for biological behaviors. In this paper, we go beyond the approach of classical acfRL and provide our agent with intrinsic motivation using empowerment. In contrast to self-empowerment, a robot employing our approach strives for the empowerment of people in its environment, so they are not disturbed by the robot's presence and motion. In our experiments, we show that our approach has a positive influence on humans, as it minimizes its distance to humans and thus decreases human travel time while moving efficiently towards its own goal. An interactive user-study shows that our method is considered more social than other state-of-the-art approaches by the participants.