Socially aware motion planning with deep reinforcement learning
Author(s)
Chen, Yu Fan; Everett, Michael F; Liu, Miao; How, Jonathan P
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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.
Date issued
2017-09Department
Massachusetts Institute of Technology. Laboratory for Information and Decision SystemsJournal
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Citation
Chen, Yu Fan, Michael Everett, Miao Liu, and Jonathan P. How. “Socially Aware Motion Planning with Deep Reinforcement Learning.” 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (September 2017).
Version: Original manuscript
ISBN
978-1-5386-2682-5