深層強化学習による社会的規範を考慮した移動計画
Socially Aware Motion Planning with Deep Reinforcement Learning
歩行者が多い環境で、社会的規範に反する行動を避けるよう深層強化学習でナビゲーションポリシーを学習し、人混みを自律走行できるようにした論文。
著者: Yu Fan Chen, Michael Everett, Miao Liu, Jonathan P. How
分類: cs.RO, cs.AI, cs.HC
原文アブストラクト
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.
