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Reinforcement learning (RL) enables social robots to generate trajectories without relying on human-designed rules or interventions, making it generally more effective than rule-based systems in adapting to complex, dynamic real-world scenarios.
Social force model for pedestrian dynamics
Dirk Helbing and Peter Molnar · 1995
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Reciprocal velocity obstacles for real-time multi-agent navigation
Jur Van den Berg, Ming Lin, and Dinesh Manocha · 2008
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High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2015
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Simple online and realtime tracking
Alex Bewley, Zongyuan Ge, Lionel Ott, Fabio Ramos, and Ben Upcroft · 2016
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Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning
Yu Fan Chen, Miao Liu, Michael Everett, and Jonathan P How · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Crowd-robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning
Changan Chen, Yuejiang Liu, Sven Kreiss, and Alexandre Alahi · 2019
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Benchmarking safe exploration in deep reinforcement learning
Alex Ray, Joshua Achiam, and Dario Amodei · 2019
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Dr-spaam: A spatial-attention and auto-regressive model for person detection in 2d range data
Dan Jia, Alexander Hermans, and Bastian Leibe · 2020
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Evolvegraph: Multi-agent trajectory prediction with dynamic relational reasoning
Jiachen Li, Fan Yang, Masayoshi Tomizuka, and Chiho Choi · 2020
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Robot navigation in crowded environments using deep reinforcement learning
Lucia Liu, Daniel Dugas, Gianluca Cesari, Roland Siegwart, and Renaud Dubé · 2020
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What the constant velocity model can teach us about pedestrian motion prediction
Christoph Schöller, Vincent Aravantinos, Florian Lay, and Alois Knoll · 2020
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Where to go next: Learning a subgoal recommendation policy for navigation in dynamic environments
Bruno Brito, Michael Everett, Jonathan P How, and Javier Alonso-Mora · 2021
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Adaptive conformal inference under distribution shift
Isaac Gibbs and Emmanuel Candes · 2021
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Loki: Long term and key intentions for trajectory prediction
Harshayu Girase, Haiming Gang, Srikanth Malla, Jiachen Li, Akira Kanehara, Karttikeya Mangalam, and Chiho Choi · 2021
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Learning sparse interaction graphs of partially detected pedestrians for trajectory prediction
Zhe Huang, Ruohua Li, Kazuki Shin, and Katherine Driggs-Campbell · 2021
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Robot navigation in constrained pedestrian environments using reinforcement learning
Claudia Pérez-D’Arpino, Can Liu, Patrick Goebel, Roberto Martín-Martín, and Silvio Savarese · 2021
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Conformal policy learning for sensorimotor control under distribution shifts
Huang Huang, Satvik Sharma, Antonio Loquercio, Anastasios Angelopoulos, Ken Goldberg, and Jitendra Malik · 2023
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Omnisafe: An infrastructure for accelerating safe reinforcement learning research
Jiaming Ji, Jiayi Zhou, Borong Zhang, Juntao Dai, Xuehai Pan, Ruiyang Sun, Weidong Huang, Yiran Geng, Mickel Liu, and Yaodong Yang · 2023
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Pedestrian crossing action recognition and trajectory prediction with 3d human keypoints
Jiachen Li, Xinwei Shi, Feiyu Chen, Jonathan Stroud, Zhishuai Zhang, Tian Lan, Junhua Mao, Jeonhyung Kang, Khaled S Refaat, Weilong Yang, et al · 2023
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Game theory-based simultaneous prediction and planning for autonomous vehicle navigation in crowded environments
Kunming Li, Yijun Chen, Mao Shan, Jiachen Li, Stewart Worrall, and Eduardo Nebot · 2023
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Socially compliant robot navigation in crowded environment by human behavior resemblance using deep reinforcement learning
Sunil Srivatsav Samsani and Mannan Saeed Muhammad · 2021
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Safe reinforcement learning using black-box reachability analysis
Mahmoud Selim, Amr Alanwar, Shreyas Kousik, Grace Gao, Marco Pavone, and Karl H Johansson · 2022
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Sauté rl: Almost surely safe reinforcement learning using state augmentation
Aivar Sootla, Alexander I Cowen-Rivers, Taher Jafferjee, Ziyan Wang, David H Mguni, Jun Wang, and Haitham Ammar · 2022
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Towards safe reinforcement learning with a safety editor policy
Haonan Yu, Wei Xu, and Haichao Zhang · 2022
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Grouptron: Dynamic multi-scale graph convolutional networks for group-aware dense crowd trajectory forecasting
Rui Zhou, Hongyu Zhou, Huidong Gao, Masayoshi Tomizuka, Jiachen Li, and Zhuo Xu · 2022
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Conformal prediction: A gentle introduction
Anastasios N Angelopoulos, Stephen Bates, et al · 2023
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Adaptive conformal prediction for motion planning among dynamic agents
Anushri Dixit, Lars Lindemann, Skylar X Wei, Matthew Cleaveland, George J Pappas, and Joel W Burdick · 2023
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Shuijing Liu, Peixin Chang, Zhe Huang, Neeloy Chakraborty, Kaiwen Hong, Weihang Liang, D Livingston McPherson, Junyi Geng, and Katherine Driggs-Campbell · 2023
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Conformal predictive safety filter for rl controllers in dynamic environments
Kegan J Strawn, Nora Ayanian, and Lars Lindemann · 2023
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Safe perception-based control under stochastic sensor uncertainty using conformal prediction
Shuo Yang, George J Pappas, Rahul Mangharam, and Lars Lindemann · 2023
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A safe reinforcement learning approach for autonomous navigation of mobile robots in dynamic environments
Zhiqian Zhou, Junkai Ren, Zhiwen Zeng, Junhao Xiao, Xinglong Zhang, Xian Guo, Zongtan Zhou, and Huimin Lu · 2023
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Conformal inference for online prediction with arbitrary distribution shifts
Isaac Gibbs and Emmanuel J Candès · 2024
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Uncertainty-aware drl for autonomous vehicle crowd navigation in shared space
Mahsa Golchoubian, Moojan Ghafurian, Kerstin Dautenhahn, and Nasser Lashgarian Azad · 2024
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Scene informer: Anchor-based occlusion inference and trajectory prediction in partially observable environments
Bernard Lange, Jiachen Li, and Mykel J Kochenderfer · 2024
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A survey on socially aware robot navigation: Taxonomy and future challenges
Phani Teja Singamaneni, Pilar Bachiller-Burgos, Luis J Manso, Anaís Garrell, Alberto Sanfeliu, Anne Spalanzani, and Rachid Alami · 2024
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