Socially Aware Motion Planning with Deep Reinforcement Learning

TL;DR

This paper presents a deep reinforcement learning approach for socially aware robotic navigation that respects human social norms, enabling autonomous movement in pedestrian-rich environments.

Contribution

It introduces a novel RL-based method focusing on avoiding social norm violations rather than modeling detailed human behaviors, improving generalization and efficiency.

Findings

Enables autonomous navigation at human walking speed among pedestrians.

Respects common social norms in complex environments.

Outperforms feature-matching approaches in generalization.

Abstract

For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging to directly specify the details of what to do (precise mechanisms of human navigation), it is straightforward to specify what not to do (violations of social norms). Specifically, using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms. The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.