📚 A curated collection of resources on social navigation, including papers, code, and tools.
👤Created and maintained by Rashid Alyassi (ralyassi@ethz.ch), ETH Zurich.
📩 If you notice any missing papers or resources, feel free to email me or submit a request.
📖 Resources are based on findings from our survey paper: "Social Robot Navigation: A Review and Benchmarking of Learning-Based Methods".
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Learning to navigate through complex dynamic environment with modular deep reinforcement learning
Wang, Y., He, H. and Sun, C.
IEEE Transactions on Games, 2018. [Paper] -
Learning a State Representation and Navigation in Cluttered and Dynamic Environments
Hoeller, D., Wellhausen, L., Farshidian, F. and Hutter, M.
IEEE RAL, 2021. [Paper] [Video] -
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Long, P., Fan, T., Liao, X., Liu, W., Zhang, H. and Pan, J.
ICRA, 2018. [Paper] [Website] -
CrowdMove: Autonomous Mapless Navigation in Crowded Scenarios
Fan, T., Cheng, X., Pan, J., Manocha, D. and Yang, R.
arXiv, 2018. [Paper] [Website] [Video] -
Crowd-Steer: Realtime Smooth and Collision-Free Robot Navigation in Densely Crowded Scenarios Trained using High-Fidelity Simulation
Liang, J., Patel, U., Sathyamoorthy, A.J. and Manocha, D.
IJCAI, 2020. [Paper] [Video] -
Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans
Jin, J., Nguyen, N.M., Sakib, N., Graves, D., Yao, H. and Jagersand, M.
ICRA, 2020. [Paper] [Website] -
NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments
Dugas, D., Nieto, J., Siegwart, R. and Chung, J.J.
ICRA, 2021. [Paper] [Code] [Video] -
Learning World Transition Model for Socially Aware Robot Navigation
Cui, Y., Zhang, H., Wang, Y. and Xiong, R.
ICRA, 2021. [Paper] [Code] [Video] -
Deep Reinforcement Learning for Robot Collision Avoidance With Self-State-Attention and Sensor Fusion
Han, Y., Zhan, I.H., Zhao, W., Pan, J., Zhang, Z., Wang, Y. and Liu, Y.J.
IEEE RAL, 2022. [Paper] -
Deep Reinforcement Learning of Navigation in a Complex and Crowded Environment with a Limited Field of View
Choi, J., Park, K., Kim, M. and Seok, S.
ICRA, 2019. [Paper] [Video] -
Fast Adaptation of Deep Reinforcement Learning-Based Navigation Skills to Human Preference
Choi, J., Dance, C., Kim, J.E., Park, K.S., Han, J., Seo, J. and Kim, M.
ICRA, 2020. [Paper] [Website] [Video] -
Multi-objective deep reinforcement learning for crowd-aware robot navigation with dynamic human preference
Cheng, G., Wang, Y., Dong, L., Cai, W. and Sun, C.
Neural Computing and Applications, 2023. [Paper] -
Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning
Lee, K., Kim, S. and Choi, J.
ICRA, 2023. [Paper] [Code] -
A Hierarchical Deep Reinforcement Learning Framework With High Efficiency and Generalization for Fast and Safe Navigation
Zhu, W. and Hayashibe, M.
IEEE Transactions on industrial Electronics, 2022. [Paper] [Code] [Video] -
Resilient Navigation Among Dynamic Agents with Hierarchical Reinforcement Learning
Wang, S., Jiang, H. and Wang, Z.
Advances in Computer Graphics, 2021. [Paper] [Video] -
Learning Navigation Behaviors End-to-End With AutoRL
Chiang, H.T.L., Faust, A., Fiser, M. and Francis, A.
IEEE RAL, 2019. [Paper] [Video] -
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-Based Planning
Faust, A., Oslund, K., Ramirez, O., Francis, A., Tapia, L., Fiser, M. and Davidson, J.
ICRA, 2018. [Paper] [Short Video] [Video] -
Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments
Zhang, J., Springenberg, J.T., Boedecker, J. and Burgard, W.
IROS, 2017. [Paper] [Video] -
Deep reinforcement learning for indoor mobile robot path planning
Gao, J., Ye, W., Guo, J. and Li, Z.
Sensors, 2020. [Paper] -
DeepMNavigate: Deep Reinforced Multi-Robot Navigation Unifying Local & Global Collision Avoidance
Tan, Q., Fan, T., Pan, J. and Manocha, D.
IROS, 2020. [Paper] [Video] -
Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning
Chen, W., Zhou, S., Pan, Z., Zheng, H. and Liu, Y.
Applied Sciences, 2019. [Paper] -
iPlanner: Imperative Path Planning
Yang, F., Wang, C., Cadena, C. and Hutter, M.
arXiv, 2023. [Paper] [Code] [Video] -
Exploiting Proximity-Aware Tasks for Embodied Social Navigation
Cancelli, E., Campari, T., Serafini, L., Chang, A.X. and Ballan, L.
IEEE/CVF, 2023. [Paper] [Code] [Video] -
From Cognition to Precognition: A Future-Aware Framework for Social Navigation
Gong, Z., Hu, T., Qiu, R. and Liang, J.
arXiv, 2024. [Paper] [Code] [Website] [Video] -
Socially Adaptive Path Planning in Human Environments Using Inverse Reinforcement Learning
Kim, B. and Pineau, J.
Journal of Social Robotics, 2016. [Paper] -
Learning How Pedestrians Navigate: A Deep Inverse Reinforcement Learning Approach
Fahad, M., Chen, Z. and Guo, Y.
IROS, 2018. [Paper] -
From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots
Pfeiffer, M., Schaeuble, M., Nieto, J., Siegwart, R. and Cadena, C.
ICRA, 2017. [Paper] [Video] -
Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation
Long, P., Liu, W. and Pan, J.
IEEE RAL, 2017. [Paper] [Video] -
Map-based Deep Imitation Learning for Obstacle Avoidance
Liu, Y., Xu, A. and Chen, Z.
IROS, 2018. [Paper] -
Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning
Tai, L., Zhang, J., Liu, M. and Burgard, W.
ICRA, 2018. [Paper] [Code] [Video] -
DeepMoTIon: Learning to Navigate Like Humans
Hamandi, M., D’Arcy, M. and Fazli, P.
IEEE RO-MAN, 2019. [Paper] [Video] -
Toward Human-Like Social Robot Navigation: A Large-Scale, Multi-Modal, Social Human Navigation Dataset
Nguyen, D.M., Nazeri, M., Payandeh, A., Datar, A. and Xiao, X.
IROS, 2023. [Paper] [Code] [Website]
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Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning
Chen, Y.F., Liu, M., Everett, M. and How, J.P.
ICRA, 2017. [Paper] [Code] [Video] -
Socially Aware Motion Planning with Deep Reinforcement Learning
Chen, Y.F., Everett, M., Liu, M. and How, J.P.
IROS, 2017. [Paper] [Code] [Video] -
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
Everett, M., Chen, Y.F. and How, J.P.
IROS, 2018. [Paper] [Code] [Video] -
Collision Avoidance in Pedestrian-Rich Environments With Deep Reinforcement Learning
Everett, M., Chen, Y.F. and How, J.P.
IEEE Access, 2021. [Paper] [Code] [Video] -
DenseCAvoid: Real-time Navigation in Dense Crowds using Anticipatory Behaviors
Sathyamoorthy, A.J., Liang, J., Patel, U., Guan, T., Chandra, R. and Manocha, D.
ICRA, 2020. [Paper] [Video] -
Deep Imitation Learning for Autonomous Navigation in Dynamic Pedestrian Environments
Qin, L., Huang, Z., Zhang, C., Guo, H., Ang, M. and Rus, D.
ICRA, 2021. [Paper] -
Reinforcement Learned Distributed Multi-Robot Navigation With Reciprocal Velocity Obstacle Shaped Rewards
Han, R., Chen, S., Wang, S., Zhang, Z., Gao, R., Hao, Q. and Pan, J.
IEEE RAL, 2022. [Paper] [Code] [Video] -
DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles
Xie, Z. and Dames, P.
IEEE Transactions on Robotics, 2023. [Paper] [Code] [Video] -
Aligning Human Preferences with Baseline Objectives in Reinforcement Learning
Marta, D., Holk, S., Pek, C., Tumova, J. and Leite, I.
ICRA, 2023. [Paper] -
Learning Adaptive Multi-Objective Robot Navigation Incorporating Demonstrations
de Heuvel, J., Sethuraman, T. and Bennewitz, M.
arXiv, 2024. [Paper] -
Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study
de Heuvel, J., Corral, N., Bruckschen, L. and Bennewitz, M.
IEEE RO-MAN, 2022. [Paper] [Video] -
Learning Depth Vision-Based Personalized Robot Navigation From Dynamic Demonstrations in Virtual Reality
de Heuvel, J., Corral, N., Kreis, B., Conradi, J., Driemel, A. and Bennewitz, M.
IROS, 2023. [Paper] [Video]
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Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning
Chen, C., Liu, Y., Kreiss, S. and Alahi, A.
ICRA, 2019. [Paper] [Code] [Website] [Video] -
Robot Navigation in Crowded Environments Using Deep Reinforcement Learning
Liu, L., Dugas, D., Cesari, G., Siegwart, R. and Dubé, R.
IROS, 2020. [Paper] [Video] -
Robot Navigation in Crowds by Graph Convolutional Networks With Attention Learned From Human Gaze
Chen, Y., Liu, C., Shi, B.E. and Liu, M.
IEEE RAL, 2020. [Paper] [Website] [Video] -
A Generative Approach for Socially Compliant Navigation
Tsai, C.E. and Oh, J.
ICRA, 2020. [Paper] [Video] -
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning
Liu, S., Chang, P., Liang, W., Chakraborty, N. and Driggs-Campbell, K.
ICRA, 2021. [Paper] [Code] [Website] [Video] -
NaviSTAR: Socially Aware Robot Navigation with Hybrid Spatio-Temporal Graph Transformer and Preference Learning
Wang, W., Wang, R., Mao, L. and Min, B.C.
IROS, 2023. [Paper] [Code] [Website] [Video] -
Graph Relational Reinforcement Learning for Mobile Robot Navigation in Large-Scale Crowded Environments
Liu, Z., Zhai, Y., Li, J., Wang, G., Miao, Y. and Wang, H.
IEEE T-ITS, 2023. [Paper]
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Where to go Next: Learning a Subgoal Recommendation Policy for Navigation in Dynamic Environments
Brito, B., Everett, M., How, J.P. and Alonso-Mora, J.
IEEE RAL, 2021. [Paper] [Code] [Video] -
Path Planning in Dynamic Environments using Generative RNNs and Monte Carlo Tree Search
Eiffert, S., Kong, H., Pirmarzdashti, N. and Sukkarieh, S.
ICRA, 2020. [Paper] [Code] [Video] -
Relational Graph Learning for Crowd Navigation
Chen, C., Hu, S., Nikdel, P., Mori, G. and Savva, M.
IROS, 2020. [Paper] [Code] [Video] [Short Video] -
From Crowd Motion Prediction to Robot Navigation in Crowds
Poddar, S., Mavrogiannis, C. and Srinivasa, S.S.
IROS, 2023. [Paper] [Code] [Video] -
Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles
Li, K., Shan, M., Narula, K., Worrall, S. and Nebot, E.
IEEE ITSC, 2020. [Paper] -
Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph
Liu, S., Chang, P., Huang, Z., Chakraborty, N., Hong, K., Liang, W., McPherson, D.L., Geng, J. and Driggs-Campbell, K.
ICRA, 2023. [Paper] [Code] [Website] [Video]
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Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Mapless Navigation by Leveraging Prior Demonstrations
Pfeiffer, M., Shukla, S., Turchetta, M., Cadena, C., Krause, A., Siegwart, R. and Nieto, J.
IEEE RAL, 2018. [Paper] [Code] -
Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios
Fan, T., Long, P., Liu, W. and Pan, J.
Journal of Robotics Research, 2020. [Paper] [Website] -
Autonomous Mobile Robot Navigation for Complicated Environments by Switching Multiple Control Policies
Amano, K. and Kato, Y.
IECON, 2022. [Paper] -
All-in-One: A DRL-based Control Switch Combining State-of-the-art Navigation Planners
Linh, K.U., Cox, J., Buiyan, T. and Lambrecht, J.
ICRA, 2022. [Paper] [Code] -
Intent-Aware Pedestrian Prediction for Adaptive Crowd Navigation
Katyal, K.D., Hager, G.D. and Huang, C.M.
ICRA, 2020. [Paper] -
Crowd Navigation in an Unknown and Dynamic Environment Based on Deep Reinforcement Learning
Sun, L., Zhai, J. and Qin, W.
IEEE Access, 2019. [Paper] -
Safe Reinforcement Learning with Model Uncertainty Estimates
Lütjens, B., Everett, M. and How, J.P.
ICRA, 2019. [Paper] [Video] -
Frozone: Freezing-Free, Pedestrian-Friendly Navigation in Human Crowds
Sathyamoorthy, A.J., Patel, U., Guan, T. and Manocha, D.
IEEE RAL, 2020. [Paper] [Video] -
XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Trees
Roth, A.M., Liang, J. and Manocha, D.
IROS, 2021. [Paper] [Code] [Website] -
L2B: Learning to Balance the Safety-Efficiency Trade-off in Interactive Crowd-aware Robot Navigation
Nishimura, M. and Yonetani, R.
IROS, 2020. [Paper] [Code] [Website] -
IAN: Multi-Behavior Navigation Planning for Robots in Real, Crowded Environments
Dugas, D., Nieto, J., Siegwart, R. and Chung, J.J.
IROS, 2020. [Paper] [Code] [Video] -
SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning
Yao, J., Zhang, X., Xia, Y., Wang, Z., Roy-Chowdhury, A.K. and Li, J.
Conference, Year. [Paper] [Code] [Video] [Website] -
Confidence-Aware Robust Dynamical Distance Constrained Reinforcement Learning for Social Robot Navigation
Zhu, K., Xue, T. and Zhang, T.
Conference, Year. [Paper] -
Social Zone as a Barrier Function for Socially-Compliant Robot Navigation
Jang, J. and Ghaffari, M.
Conference, Year. [Paper] -
A safe reinforcement learning approach for autonomous navigation of mobile robots in dynamic environments
Zhou, Z., Ren, J., Zeng, Z., Xiao, J., Zhang, X., Guo, X., Zhou, Z. and Lu, H.
Conference, Year. [Paper]
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You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A.
CVPR, 2016. [Paper] [Code] -
SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y. and Berg, A.C.
Springer International, 2016. [Paper] [Code] -
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A. and Zagoruyko, S.
Springer International Publishing, 2020. [Paper] [Code] -
DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Data
Jia, D., Hermans, A. and Leibe, B.
IROS, 2020. [Paper] [Code] -
Multiple Hypothesis Tracking Algorithm for Multi-target Multi-camera Tracking with Disjoint Views
Yoon, K., Song, Y.M. and Jeon, M.
IET Image Processing, 2018. [Paper] [Code] -
Simple Online and Realtime Tracking with a Deep Association Metric
Wojke, N., Bewley, A. and Paulus, D.
IEEE ICIP, 2017. [Paper] [Code] -
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W. and Wang, X.
ECCV, 2022. [Paper] [Code]
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Social LSTM: Human Trajectory Prediction in Crowded Spaces
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L. and Savarese, S.
CVPR, 2016. [Paper] [Code] -
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S. and Alahi, A.
CVPR, 2018. [Paper] [Code] -
SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints
Sadeghian, A., Kosaraju, V., Sadeghian, A., Hirose, N., Rezatofighi, H. and Savarese, S.
CVPR, 2019. [Paper] [Code] -
Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction Using a Graph Vehicle-Pedestrian Attention Network
Eiffert, S., Li, K., Shan, M., Worrall, S., Sukkarieh, S. and Nebot, E.
IEEE RAL, 2020. [Paper] -
Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion
Gu, T., Chen, G., Li, J., Lin, C., Rao, Y., Zhou, J. and Lu, J.
CVPR, 2022. [Paper] [Code] -
Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs
Amirian, J., Hayet, J.B. and Pettré, J.
CVPR, 2019. [Paper] [Code] -
Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction
Mohamed, A., Qian, K., Elhoseiny, M. and Claudel, C.
CVPR, 2020. [Paper] [Code]
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ETH Dataset
Pellegrini, S., Ess, A., Schindler, K. and Van Gool, L.
ICCV, 2009. [Paper] [Dataset] -
UCY Dataset
Lerner, A., Chrysanthou, Y. and Lischinski, D.
Computer graphics forum, 2007. [Paper] [Dataset] -
Central Train Station Dataset
Zhou, B., Wang, X. and Tang, X.
CVPR, 2012. [Paper] [Dataset] -
Edinburgh Form Dataset
Majecka, B.
Thesis, 2009. [Paper] [Dataset] -
Stanford Drone Dataset
Robicquet, A., Sadeghian, A., Alahi, A. and Savarese, S.
ECCV, 2019. [Paper] [Dataset] -
VIRAT Dataset
Oh, S., Hoogs, A., Perera, A., Cuntoor, N., Chen, C.C., Lee, J.T., Mukherjee, S., Aggarwal, J.K., Lee, H., Davis, L. and Swears, E.
CVPR, 2011. [Paper] [Dataset] -
Oxford Town-Center Dataset
Benfold, B. and Reid, I.
CVPR, 2011. [Paper] [Dataset] -
ATC Dataset
Brščić, D., Kanda, T., Ikeda, T. and Miyashita, T.
IEEE THMS, 2013. [Paper] [Dataset] -
Ko-PER Dataset
Strigel, E., Meissner, D., Seeliger, F., Wilking, B. and Dietmayer, K.
ITSC, 2014. [Paper] [Dataset] -
inD Dataset
Bock, J., Krajewski, R., Moers, T., Runde, S., Vater, L. and Eckstein, L.
IEEE IV, 2020. [Paper] [Dataset] -
CITR and DUT Dataset
Yang, D., Li, L., Redmill, K. and Özgüner, Ü.
IEEE IV, 2019. [Paper] [CITR Dataset] [DUT Dataset] -
WILDTRACK Dataset
Chavdarova, T., Baqué, P., Bouquet, S., Maksai, A., Jose, C., Bagautdinov, T., Lettry, L., Fua, P., Van Gool, L. and Fleuret, F.
CVPR, 2018. [Paper] [Dataset] -
STCrowd Dataset
Cong, P., Zhu, X., Qiao, F., Ren, Y., Peng, X., Hou, Y., Xu, L., Yang, R., Manocha, D. and Ma, Y.
CVPR, 2022. [Paper] [Dataset] [Website] -
Thor Dataset
Rudenko, A., Kucner, T.P., Swaminathan, C.S., Chadalavada, R.T., Arras, K.O. and Lilienthal, A.J.
IEEE RAL, 2020. [Paper] [Dataset] -
L-CAS Dataset
Yan, Z., Duckett, T. and Bellotto, N.
IROS, 2017. [Paper] [Dataset] -
SCAND Dataset
Karnan, H., Nair, A., Xiao, X., Warnell, G., Pirk, S., Toshev, A., Hart, J., Biswas, J. and Stone, P.
IEEE RAL, 2022. [Paper] [Dataset] -
JRDB Dataset
Martin-Martin, R., Patel, M., Rezatofighi, H., Shenoi, A., Gwak, J., Frankel, E., Sadeghian, A. and Savarese, S.
IEEE TPAMI, 2021. [Paper] [Dataset] -
FLOBOT Dataset
Yan, Z., Schreiberhuber, S., Halmetschlager, G., Duckett, T., Vincze, M. and Bellotto, N.
ISR, 2020. [Paper] [Dataset] -
NCLT Dataset
Carlevaris-Bianco, N., Ushani, A.K. and Eustice, R.M.
IJRR, 2016. [Paper] [Dataset] -
MuSoHu Dataset
Nguyen, D.M., Nazeri, M., Payandeh, A., Datar, A. and Xiao, X.
IROS, 2023. [Paper] [Dataset] -
CrowdBot Dataset
Paez-Granados, D., He, Y., Gonon, D., Huber, L., Billard, A.
ICCV, 2019. [Paper] [Dataset] -
HuRoN Dataset
Hirose, N., Shah, D., Sridhar, A. and Levine, S.
IEEE RAL, 2023. [Paper] [Dataset] -
SiT Dataset
Bae, J.W., Kim, J., Yun, J., Kang, C., Choi, J., Kim, C., Lee, J., Choi, J. and Choi, J.W.
NeurIPS, 2024. [Paper] [Dataset] [Website] -
KITTI Dataset
Geiger, A., Lenz, P. and Urtasun, R.
CVPR, 2012. [Paper] [Dataset] -
nuScences Dataset
Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G. and Beijbom, O.
CVPR, 2020. [Paper] [Dataset] -
Waymo Dataset
Ettinger, S., Cheng, S., Caine, B., Liu, C., Zhao, H., Pradhan, S., Chai, Y., Sapp, B., Qi, C.R., Zhou, Y. and Yang, Z.
ICCV, 2021. [Paper] [Dataset] -
BDD100K Dataset
Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V. and Darrell, T.
CVPR, 2020. [Paper] [Dataset] -
A2D2 Dataset
Geyer, J., Kassahun, Y., Mahmudi, M., Ricou, X., Durgesh, R., Chung, A.S., Hauswald, L., Pham, V.H., Mühlegg, M., Dorn, S. and Fernandez, T.
arXiv, 2020. [Paper] [Dataset]
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Menge
Curtis, S., Best, A. and Manocha, D.
Collective Dynamics, 2016. [Paper] [Code] -
SEAN
Tsoi, N., Xiang, A., Yu, P., Sohn, S.S., Schwartz, G., Ramesh, S., Hussein, M., Gupta, A.W., Kapadia, M. and Vázquez, M.
IEEE RAL, 2022. [Paper] [Code] -
SEAN-EP
Tsoi, N., Hussein, M., Fugikawa, O., Zhao, J.D. and Vázquez, M.
IROS, 2021. [Paper] [Code] -
UnrealCV
Qiu, W., Zhong, F., Zhang, Y., Qiao, S., Xiao, Z., Kim, T.S. and Wang, Y.
ACM-ICM, 2017. [Paper] [Code] -
MORSE
Echeverria, G., Lassabe, N., Degroote, A. and Lemaignan, S.
ICRA, 2011. [Paper] [Code] -
Isaac Sim
Mittal, M., Yu, C., Yu, Q., Liu, J., Rudin, N., Hoeller, D., Yuan, J.L., Singh, R., Guo, Y., Mazhar, H. and Mandlekar, A.
IEEE RAL, 2023. [Paper] [Code] -
AI2-THOR
Kolve, E., Mottaghi, R., Han, W., VanderBilt, E., Weihs, L., Herrasti, A., Deitke, M., Ehsani, K., Gordon, D., Zhu, Y. and Kembhavi, A.
arXiv, 2017. [Paper] [Code] -
Habitat 2.0
Szot, A., Clegg, A., Undersander, E., Wijmans, E., Zhao, Y., Turner, J., Maestre, N., Mukadam, M., Chaplot, D.S., Maksymets, O. and Gokaslan, A.
NeurIPS, 2021. [Paper] [Code] -
Habitat 3.0
Puig, X., Undersander, E., Szot, A., Cote, M.D., Yang, T.Y., Partsey, R., Desai, R., Clegg, A.W., Hlavac, M., Min, S.Y. and Vondruš, V.
arXiv, 2023. [Paper] [Code] -
HabiCrowd
Vuong, A.D., Nguyen, T.T., VU, M.N., Huang, B., Nguyen, D., Binh, H.T.T., Vo, T. and Nguyen, A.
arXiv, 2023. [Paper] [Code] -
SAPIEN
Xiang, F., Qin, Y., Mo, K., Xia, Y., Zhu, H., Liu, F., Liu, M., Jiang, H., Yuan, Y., Wang, H. and Yi, L.
CVPR, 2020. [Paper] [Code] -
CrowdBot Sim
Grzeskowiak, F., Gonon, D., Dugas, D., Paez-Granados, D., Chung, J.J., Nieto, J., Siegwart, R., Billard, A., Babel, M. and Pettré, J.
ICRA, 2021. [Paper] [Code] -
iGibson
Li, C., Xia, F., Martín-Martín, R., Lingelbach, M., Srivastava, S., Shen, B., Vainio, K., Gokmen, C., Dharan, G., Jain, T. and Kurenkov, A.
arXiv, 2021. [Paper] [Code] -
CrowdNav
Chen, C., Liu, Y., Kreiss, S. and Alahi, A.
ICRA, 2019. [Paper] [Code] -
Arena-Rosnav
Kästner, L., Buiyan, T., Jiao, L., Le, T.A., Zhao, X., Shen, Z. and Lambrecht, J.
IROS, 2021. [Paper] [Code] -
nav-gym
Lee, K., Kim, S. and Choi, J.
ICRA, 2023. [Paper] [Code] -
gym-collision-avoidance
Everett, M., Chen, Y.F. and How, J.P.
IROS, 2018. [Paper] [Code] -
navrep
Dugas, D., Nieto, J., Siegwart, R. and Chung, J.J.
ICRA, 2021. [Paper] [Code] -
gym ped sim
Tai, L., Zhang, J., Liu, M. and Burgard, W.
ICRA, 2018. [Paper] [Code] -
Social Gym
Sprague, Z., Chandra, R., Holtz, J. and Biswas, J.
arXiv, 2023. [Paper] [Code] -
RDS Sim
Gonon, D.J., Paez-Granados, D. and Billard, A.
IEEE RAL, 2021. [Paper] [Code]
-
RVO2
Van Den Berg, J., Guy, S.J., Snape, J. Lin, M. and Manocha, D.
[Code (C++)] [Code (Python)] -
UMANS
van Toll, W., Grzeskowiak, F., Gandía, A.L., Amirian, J., Berton, F., Bruneau, J., Daniel, B.C., Jovane, A. and Pettré, J.
I3D, 2020. [Paper] [Code] [Website] -
PySocialForce
Gao, Y. [Code] -
CrowdDynamics
Group, C.D. [Code] -
JuPedSim
Dynamics, P. [Code] -
Mesa
Masad, D. and Kazil, J.L.
SciPy, 2015. [Paper] [Code] [Website] -
Agents.jl
Datseris, G., Vahdati, A.R. and DuBois, T.C.
Simulation, 2024, [Paper] [Code] [Website] -
Vadere
Kleinmeier, B., Zönnchen, B., Gödel, M. and Köster, G.
arXiv, 2019. [Paper] [Code] [Website]
To add a new paper via pull request: Fork the repo, edit README.md, and add the new paper at the correct position with the following format.
* **Paper Title** <br>
*Authors (Harvard Style)* <br>
Conference, Year. [[Paper]](link) [[Code]](link) [[Video]](link) [[Website]](link)