# Learning Contact-based Navigation in Crowds

arXiv: https://arxiv.org/abs/2303.01455 · HTML: https://mlsanity.com/paper/2303.01455/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 2 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Combining Optimal Control and Learning for Visual Navigation in Novel Environments](https://mlsanity.com/paper/1903.02531/) (2019)
- [Hierarchical Game-Theoretic Planning for Autonomous Vehicles](https://mlsanity.com/paper/1810.05766/) (2018)
- [Frozone: Freezing-Free, Pedestrian-Friendly Navigation in Human Crowds](https://mlsanity.com/paper/2003.05395/) (2020)
- [Conflict Avoidance in Social Navigation -- a Survey](https://mlsanity.com/paper/2106.12113/) (2021)
- [Visual Navigation Among Humans with Optimal Control as a Supervisor](https://mlsanity.com/paper/2003.09354/) (2020)
- [Learning to Walk by Steering: Perceptive Quadrupedal Locomotion in Dynamic Environments](https://mlsanity.com/paper/2209.09233/) (2022)
- [Pedestrian-Robot Interactions on Autonomous Crowd Navigation: Reactive Control Methods and Evaluation Metrics](https://mlsanity.com/paper/2208.02121/) (2022)
- [Unfreezing Social Navigation: Dynamical Systems based Compliance for Contact Control in Robot Navigation](https://mlsanity.com/paper/2203.01053/) (2022)
- [Full-Body Collision Detection and Reaction with Omnidirectional Mobile Platforms: A Step Towards Safe Human-Robot Interaction](https://mlsanity.com/paper/1501.05007/) (2015)

## Abstract (arXiv)

Navigation strategies that intentionally incorporate contact with humans (i.e. "contact-based" social navigation) in crowded environments are largely unexplored even though collision-free social navigation is a well studied problem. Traditional social navigation frameworks require the robot to stop suddenly or "freeze" whenever a collision is imminent. This paradigm poses two problems: 1) freezing while navigating a crowd may cause people to trip and fall over the robot, resulting in more harm than the collision itself, and 2) in very dense social environments where collisions are unavoidable, such a control scheme would render the robot unable to move and preclude the opportunity to study how humans incorporate robots into these environments. However, if robots are to be meaningfully included in crowded social spaces, such as busy streets, subways, stores, or other densely populated locales, there may not exist trajectories that can guarantee zero collisions. Thus, adoption of robots in these environments requires the development of minimally disruptive navigation plans that can safely plan for and respond to contacts. We propose a learning-based motion planner and control scheme to navigate dense social environments using safe contacts for an omnidirectional mobile robot. The planner is evaluated in simulation over 360 trials with crowd densities varying between 0.0 and 1.6 people per square meter. Our navigation scheme is able to use contact to safely navigate in crowds of higher density than has been previously reported, to our knowledge.