Fetching the paper…
Reading the bibliography…
The recent advancements in visual reasoning capabilities of large multimodal models (LMMs) and the semantic enrichment of 3D feature fields have expanded the horizons of robotic capabilities.
2003
Earlier work this paper cites.
2021
Earlier work this paper cites.
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in Proceedings of the International Conference on Computer Vision (ICCV) , 2021
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.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
S. S. Raman, V. Cohen, E. Rosen, I. Idrees, D. Paulius, and S. Tellex, “Planning with large language models via corrective re-prompting,” in NeurIPS 2022 Foundation Models for Decision Making Workshop , 2022
2022
Earlier work this paper cites.
W. Huang, P. Abbeel, D. Pathak, and I. Mordatch, “Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,” in International conference on machine learning . PMLR, 2022, pp. 9118–9147
2022
Earlier work this paper cites.
G. Qian, Y. Li, H. Peng, J. Mai, H. Hammoud, M. Elhoseiny, and B. Ghanem, “Pointnext: Revisiting pointnet++ with improved training and scaling strategies,” Advances in Neural Information Processing Systems , vol. 35, pp. 23 192–23 204, 2022
2022
Earlier work this paper cites.
P. Florence, C. Lynch, A. Zeng, O. A. Ramirez, A. Wahid, L. Downs, A. Wong, J. Lee, I. Mordatch, and J. Tompson, “Implicit behavioral cloning,” in Conference on Robot Learning . PMLR, 2022, pp. 158–168
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2023
Earlier work this paper cites.
C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y. Su, “Llm-planner: Few-shot grounded planning for embodied agents with large language models,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 2998–3009
2023
Earlier work this paper cites.
I. Singh, V. Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and A. Garg, “Progprompt: Generating situated robot task plans using large language models,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 11 523–11 530
2023
Earlier work this paper cites.
N. Wake, A. Kanehira, K. Sasabuchi, J. Takamatsu, and K. Ikeuchi, “Chatgpt empowered long-step robot control in various environments: A case application,” IEEE Access , 2023
2023
Earlier work this paper cites.
B. Chen, F. Xia, B. Ichter, K. Rao, K. Gopalakrishnan, M. S. Ryoo, A. Stone, and D. Kappler, “Open-vocabulary queryable scene representations for real world planning,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 11 509–11 522
2023
Earlier work this paper cites.
J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng, “Code as policies: Language model programs for embodied control,” 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
M. Shridhar, L. Manuelli, and D. Fox, “Perceiver-actor: A multi-task transformer for robotic manipulation,” in Conference on Robot Learning . PMLR, 2023, pp. 785–799
2023
Later among the works it cites.
Y. Ze, G. Yan, Y.-H. Wu, A. Macaluso, Y. Ge, J. Ye, N. Hansen, L. E. Li, and X. Wang, “Gnfactor: Multi-task real robot learning with generalizable neural feature fields,” in Conference on Robot Learning . PMLR, 2023, pp. 284–301
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2023
Cited alongside, same era.
2023
Cited alongside, same era.
OpenAI. (2023) Gpt-4v(ision) system card. [Online]. Available: https://cdn.openai.com/papers/GPTV_System_Card.pdf
2023
Cited alongside, same era.
J. Wu, R. Antonova, A. Kan, M. Lepert, A. Zeng, S. Song, J. Bohg, S. Rusinkiewicz, and T. Funkhouser, “Tidybot: Personalized robot assistance with large language models,” Autonomous Robots , vol. 47, no. 8, pp. 1087–1102, 2023
2023
Cited alongside, same era.
K. Lin, C. Agia, T. Migimatsu, M. Pavone, and J. Bohg, “Text2motion: From natural language instructions to feasible plans,” Autonomous Robots , vol. 47, no. 8, pp. 1345–1365, 2023
2023
Cited alongside, same era.
S. Vemprala, R. Bonatti, A. Bucker, and A. Kapoor, “Chatgpt for robotics: Design principles and model abilities,” 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” 2023
2023
Later among the works it cites.
T. Gervet, Z. Xian, N. Gkanatsios, and K. Fragkiadaki, “Act3d: 3d feature field transformers for multi-task robotic manipulation,” 2023
2023
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
F. Liu, K. Fang, P. Abbeel, and S. Levine, “Moka: Open-vocabulary robotic manipulation through mark-based visual prompting,” 2024
2024
Later among the works it cites.
D. Honerkamp, M. Büchner, F. Despinoy, T. Welschehold, and A. Valada, “Language-grounded dynamic scene graphs for interactive object search with mobile manipulation,” IEEE Robotics and Automation Letters , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
M. Minderer, A. Gritsenko, and N. Houlsby, “Scaling open-vocabulary object detection,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Later among the works it cites.