The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Socially aware motion planning with deep reinforcement learning
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.
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Cites
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Planning and Decision-Making for Autonomous Vehicles
Schwarting, W., Alonso-Mora, J., & Rus, D. (2018) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-060117-105157 · Cited by 972
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Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Long, P., Fanl, T., Liao, X., Liu, W., Zhang, H., & Pan, J. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8461113 · Cited by 538
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Metadata
- Authors
- Chen, Y. F., Everett, M., Liu, M., & How, J. P.
- Venue
- 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2017-09
- Pages
- 1343-1350
- OpenAlex
- W2604216058
- Cited by
- 719
- References
- 24
- FWCI
- 24.39
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chen, Y. F., Everett, M., Liu, M., & How, J. P. (2017). Socially aware motion planning with deep reinforcement learning. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/iros.2017.8202312
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