{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/socially-aware-motion-planning-with-deep","title":"Socially Aware Motion Planning with Deep Reinforcement Learning","arxiv_id":"1703.08862","date":"2017-03-26","proceeding":null,"authors":["Yu Fan Chen","Michael Everett","Miao Liu","Jonathan P. How"],"abstract":"For robotic vehicles to navigate safely and efficiently in pedestrian-rich\nenvironments, it is important to model subtle human behaviors and navigation\nrules (e.g., passing on the right). However, while instinctive to humans,\nsocially compliant navigation is still difficult to quantify due to the\nstochasticity in people's behaviors. Existing works are mostly focused on using\nfeature-matching techniques to describe and imitate human paths, but often do\nnot generalize well since the feature values can vary from person to person,\nand even run to run. This work notes that while it is challenging to directly\nspecify the details of what to do (precise mechanisms of human navigation), it\nis straightforward to specify what not to do (violations of social norms).\nSpecifically, using deep reinforcement learning, this work develops a\ntime-efficient navigation policy that respects common social norms. The\nproposed method is shown to enable fully autonomous navigation of a robotic\nvehicle moving at human walking speed in an environment with many pedestrians.","url_abs":"http://arxiv.org/abs/1703.08862v2","url_pdf":"http://arxiv.org/pdf/1703.08862v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"socially-aware-motion-planning-with-deep","repo_url":"https://github.com/miaoruonan/MACA_test","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"socially-aware-motion-planning-with-deep","repo_url":"https://github.com/mit-acl/gym-collision-avoidance","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"autonomous-navigation","task_name":"Autonomous Navigation"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"motion-planning","task_name":"Motion Planning"},{"task_slug":"navigate","task_name":"Navigate"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1703.08862","atlas_url":"https://app.syntology.ai/?focus=1703.08862","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}