Fetching the paper…
Reading the bibliography…
We present BehAV, a novel approach for autonomous robot navigation in outdoor scenes guided by human instructions and leveraging Vision Language Models (VLMs).
H. Alt and M. Godau, “Computing the fréchet distance between two polygonal curves,” International Journal of Computational Geometry & Applications , vol. 5, no. 01n02, pp. 75–91, 1995
1995
Earlier work this paper cites.
D. Silver, J. A. Bagnell, and A. Stentz, “Applied imitation learning for autonomous navigation in complex natural terrain,” in Field and Service Robotics: Results of the 7th International Conference . Springer, 2010, pp. 249–259
2010
Earlier work this paper cites.
J. J. Park, C. Johnson, and B. Kuipers, “Robot navigation with model predictive equilibrium point control,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2012, pp. 4945–4952
2012
Earlier work this paper cites.
H. S. Park, T. Shiratori, I. Matthews, and Y. Sheikh, “3d trajectory reconstruction under perspective projection,” International Journal of Computer Vision , vol. 115, pp. 115–135, 2015
2015
Earlier work this paper cites.
L. Tai, J. Zhang, M. Liu, and W. Burgard, “Sociosense: Robot navigation amongst pedestrians with social and psychological constraints,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 7018–7025
2018
Earlier work this paper cites.
C. I. Mavrogiannis, W. B. Thomason, and R. A. Knepper, “Social momentum: A framework for legible navigation in dynamic multi-agent environments,” in Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction , 2018, pp. 361–369
2018
Earlier work this paper cites.
C. Chen, Y. Liu, S. Kreiss, and A. Alahi, “Crowd-robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning,” in 2019 international conference on robotics and automation (ICRA) . IEEE, 2019, pp. 6015–6022
2019
Earlier work this paper cites.
C. Wang, L. Yin, Q. Zhao, W. Wang, C. Li, and B. Luo, “An intelligent robot for indoor substation inspection,” Industrial Robot: the international journal of robotics research and application , vol. 47, no. 5, pp. 705–712, 2020
2020
Earlier work this paper cites.
T. B. Brown, “Language models are few-shot learners,” arXiv preprint arXiv:2005.14165 , 2020
2020
Earlier work this paper cites.
D. Lee, G. Kang, B. Kim, and D. H. Shim, “Assistive delivery robot application for real-world postal services,” IEEE Access , vol. 9, pp. 141 981–141 998, 2021
2021
Earlier work this paper cites.
A. J. Sathyamoorthy, U. Patel, M. Paul, Y. Savle, and D. Manocha, “Covid surveillance robot: Monitoring social distancing constraints in indoor scenarios,” Plos one , vol. 16, no. 12, p. e0259713, 2021
2021
Earlier work this paper cites.
J. Liang, U. Patel, A. J. Sathyamoorthy, and D. Manocha, “Crowd-steer: Realtime smooth and collision-free robot navigation in densely crowded scenarios trained using high-fidelity simulation,” in Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence , 2021, pp. 4221–4228
2021
Earlier work this paper cites.
S. H. Kiss, K. Katuwandeniya, A. Alempijevic, and T. Vidal-Calleja, “Probabilistic dynamic crowd prediction for social navigation,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 9269–9275
2021
Earlier work this paper cites.
L. Qin, Z. Huang, C. Zhang, H. Guo, M. Ang, and D. Rus, “Deep imitation learning for autonomous navigation in dynamic pedestrian environments,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 4108–4115
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
U. Patel, N. K. S. Kumar, A. J. Sathyamoorthy, and D. Manocha, “Dwa-rl: Dynamically feasible deep reinforcement learning policy for robot navigation among mobile obstacles,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 6057–6063
2021
Earlier work this paper cites.
T. Guan, D. Kothandaraman, R. Chandra, A. J. Sathyamoorthy, K. Weerakoon, and D. Manocha, “Ga-nav: Efficient terrain segmentation for robot navigation in unstructured outdoor environments,” IEEE Robotics and Automation Letters , vol. 7, no. 3, pp. 8138–8145, 2022
2022
Earlier work this paper cites.
A. J. Sathyamoorthy, K. Weerakoon, T. Guan, J. Liang, and D. Manocha, “Terrapn: Unstructured terrain navigation using online self-supervised learning,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 7197–7204
2022
Earlier work this paper cites.
K. Weerakoon, A. J. Sathyamoorthy, U. Patel, and D. Manocha, “Terp: Reliable planning in uneven outdoor environments using deep reinforcement learning,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 9447–9453
2022
Earlier work this paper cites.
T. Lüddecke and A. Ecker, “Image segmentation using text and image prompts,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 7086–7096
2022
Cited alongside, same era.
R. Schmid, D. Atha, F. Schöller, S. Dey, S. Fakoorian, K. Otsu, B. Ridge, M. Bjelonic, L. Wellhausen, M. Hutter, et al. , “Self-supervised traversability prediction by learning to reconstruct safe terrain,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 12 419–12 425
2022
Cited alongside, same era.
H. Karnan, A. Nair, X. Xiao, G. Warnell, S. Pirk, A. Toshev, J. Hart, J. Biswas, and P. Stone, “Socially compliant navigation dataset (scand): A large-scale dataset of demonstrations for social navigation,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 11 807–11 814, 2022
2022
Cited alongside, same era.
H. Ha and S. Song, “Semantic abstraction: Open-world 3D scene understanding from 2D vision-language models,” in Proceedings of the 2022 Conference on Robot Learning , 2022
S. Jung, J. Lee, X. Meng, B. Boots, and A. Lambert, “V-strong: Visual self-supervised traversability learning for off-road navigation,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024, pp. 1766–1773
2024
Closest in time.
2024
Closest in time.
L. Tang, P.-T. Jiang, Z. Shen, H. Zhang, J. Chen, and B. Li, “Chain of visual perception: Harnessing multimodal large language models for zero-shot camouflaged object detection,” in ACM Multimedia 2024 , 2024
2024
Closest in time.
2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
A. Majumdar, G. Aggarwal, B. Devnani, J. Hoffman, and D. Batra, “Zson: Zero-shot object-goal navigation using multimodal goal embeddings,” Advances in Neural Information Processing Systems , vol. 35, pp. 32 340–32 352, 2022
2022
Cited alongside, same era.
K. Weerakoon, A. J. Sathyamoorthy, J. Liang, T. Guan, U. Patel, and D. Manocha, “Graspe: Graph based multimodal fusion for robot navigation in outdoor environments,” IEEE Robotics and Automation Letters , 2023
2023
Cited alongside, same era.
H. Karnan, E. Yang, D. Farkash, G. Warnell, J. Biswas, and P. Stone, “Sterling: Self-supervised terrain representation learning from unconstrained robot experience,” in 7th Annual Conference on Robot Learning , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
C. Huang, O. Mees, A. Zeng, and W. Burgard, “Audio visual language maps for robot navigation,” in International Symposium on Experimental Robotics . Springer, 2023, pp. 105–117
2023
Cited alongside, same era.
B. Zitkovich, T. Yu, S. Xu, P. Xu, T. Xiao, F. Xia, J. Wu, P. Wohlhart, S. Welker, A. Wahid, et al. , “Rt-2: Vision-language-action models transfer web knowledge to robotic control,” in Conference on Robot Learning . PMLR, 2023, pp. 2165–2183
2023
Cited alongside, same era.
2023
Cited alongside, same era.
A. Sridhar, D. Shah, C. Glossop, and S. Levine, “NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration,” arXiv pre-print , 2023. [Online]. Available: https://arxiv.org/abs/2310.xxxx
2023
Cited alongside, same era.
A. H. Raj, Z. Hu, H. Karnan, R. Chandra, A. Payandeh, L. Mao, P. Stone, J. Biswas, and X. Xiao, “Rethinking social robot navigation: Leveraging the best of two worlds,” in 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE , 2024
2024
Closest in time.
Y. Dai, R. Peng, S. Li, and J. Chai, “Think, act, and ask: Open-world interactive personalized robot navigation,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024, pp. 3296–3303
2024
Closest in time.
2024
Closest in time.
A. Nagar, S. Jaiswal, and C. Tan, “Zero-shot visual reasoning by vision-language models: Benchmarking and analysis,” in 2024 International Joint Conference on Neural Networks (IJCNN) , 2024, pp. 1–8
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
M. Elnoor, A. J. Sathyamoorthy, K. Weerakoon, and D. Manocha, “Pronav: Proprioceptive traversability estimation for legged robot navigation in outdoor environments,” IEEE Robotics and Automation Letters , vol. 9, no. 8, pp. 7190–7197, 2024
2024
Closest in time.
S. Jung, J. Lee, X. Meng, B. Boots, and A. Lambert, “V-strong: Visual self-supervised traversability learning for off-road navigation,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 1766–1773
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
S. H. Arul, J. J. Park, V. Prem, Y. Zhang, and D. Manocha, “Unconstrained model predictive control for robot navigation under uncertainty,” 2024
2024
Closest in time.