{"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/crowd-robot-interaction-crowd-aware-robot","title":"Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning","arxiv_id":"1809.08835","date":"2018-09-24","proceeding":null,"authors":["Changan Chen","Yuejiang Liu","Sven Kreiss","Alexandre Alahi"],"abstract":"Mobility in an effective and socially-compliant manner is an essential yet\nchallenging task for robots operating in crowded spaces. Recent works have\nshown the power of deep reinforcement learning techniques to learn socially\ncooperative policies. However, their cooperation ability deteriorates as the\ncrowd grows since they typically relax the problem as a one-way Human-Robot\ninteraction problem. In this work, we want to go beyond first-order Human-Robot\ninteraction and more explicitly model Crowd-Robot Interaction (CRI). We propose\nto (i) rethink pairwise interactions with a self-attention mechanism, and (ii)\njointly model Human-Robot as well as Human-Human interactions in the deep\nreinforcement learning framework. Our model captures the Human-Human\ninteractions occurring in dense crowds that indirectly affects the robot's\nanticipation capability. Our proposed attentive pooling mechanism learns the\ncollective importance of neighboring humans with respect to their future\nstates. Various experiments demonstrate that our model can anticipate human\ndynamics and navigate in crowds with time efficiency, outperforming\nstate-of-the-art methods.","url_abs":"http://arxiv.org/abs/1809.08835v2","url_pdf":"http://arxiv.org/pdf/1809.08835v2.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":"crowd-robot-interaction-crowd-aware-robot","repo_url":"https://github.com/vita-epfl/CrowdNav","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"crowd-robot-interaction-crowd-aware-robot","repo_url":"https://github.com/vita-epfl/DyNav","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"crowd-robot-interaction-crowd-aware-robot","repo_url":"https://github.com/neurobot-project/AvoidanceSimulation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"crowd-robot-interaction-crowd-aware-robot","repo_url":"https://github.com/raise-lab/soc-nav-training","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"crowd-robot-interaction-crowd-aware-robot","repo_url":"https://github.com/romi514/TLSGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"crowd-robot-interaction-crowd-aware-robot","repo_url":"https://github.com/sepsamavi/safe-interactive-crowdnav","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"crowd-robot-interaction-crowd-aware-robot","repo_url":"https://github.com/tessavdheiden/SCR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"human-dynamics","task_name":"Human Dynamics"},{"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":"robot-navigation","task_name":"Robot Navigation"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.08835","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.08835"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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