# Follow-Me in Micro-Mobility with End-to-End Imitation Learning

arXiv: https://arxiv.org/abs/2511.05158 · HTML: https://mlsanity.com/paper/2511.05158/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 7 Nov 2025.

## Placed in

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

## Builds on (most cited first, 8 of 8)

- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges](https://mlsanity.com/paper/2309.02473/) (2023)
- [Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety](https://mlsanity.com/paper/1903.00640/) (2019)
- [Active Perception based Formation Control for Multiple Aerial Vehicles](https://mlsanity.com/paper/1901.07813/) (2019)
- [FollowMe: a Robust Person Following Framework Based on Re-Identification and Gestures](https://mlsanity.com/paper/2311.12992/) (2023)
- [Obstacle-Avoidant Leader Following with a Quadruped Robot](https://mlsanity.com/paper/2410.00572/) (2024)
- [Exploiting Redundancy for UWB Anomaly Detection in Infrastructure-Free Multi-Robot Relative Localization](https://mlsanity.com/paper/2303.17207/) (2023)

## Built on by (most cited first, 1 of 1)

- [Sim-to-Real Aware End-to-End Learning Environment for Micromobility](https://mlsanity.com/paper/2609.28969/) (2026)

## Abstract (arXiv)

Autonomous micro-mobility platforms face challenges from the perspective of the typical deployment environment: large indoor spaces or urban areas that are potentially crowded and highly dynamic. While social navigation algorithms have progressed significantly, optimizing user comfort and overall user experience over other typical metrics in robotics (e.g., time or distance traveled) is understudied. Specifically, these metrics are critical in commercial applications. In this paper, we show how imitation learning delivers smoother and overall better controllers, versus previously used manually-tuned controllers. We demonstrate how DAAV's autonomous wheelchair achieves state-of-the-art comfort in follow-me mode, in which it follows a human operator assisting persons with reduced mobility (PRM). This paper analyzes different neural network architectures for end-to-end control and demonstrates their usability in real-world production-level deployments.