Socially aware motion planning with deep reinforcement learning
Name
1703.08862.pdf
Size
3.66 MB
Format
Adobe PDF
Checksum (MD5)
6b3ab29340f93047199c72895abc1663
Author(s)
Chen, Yu Fan
Everett, Michael F
Liu, Miao
How, Jonathan P
Date Issued
September 2017
Journal
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
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.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/IROS.2017.8202312
