Socially Aware Motion Planning with Deep Reinforcement Learning
This addresses the challenge of socially compliant navigation for robotic vehicles in crowded settings, representing an incremental improvement over existing feature-matching methods.
The paper tackled the problem of enabling robotic vehicles to navigate in pedestrian-rich environments by respecting social norms, using deep reinforcement learning to develop a time-efficient navigation policy that avoids norm violations, resulting in fully autonomous navigation at human walking speed.
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.