Fetching the paper…
Reading the bibliography…
Leveraging the powerful reasoning capabilities of large language models (LLMs), recent LLM-based robot task planning methods yield promising results.
C. Aeronautiques, A. Howe, C. Knoblock, I. D. McDermott, A. Ram, M. Veloso, D. Weld, D. W. SRI, A. Barrett, D. Christianson et al. , “Pddl— the planning domain definition language,” Technical Report, Tech. Rep. , 1998
1998
Earlier work this paper cites.
J. Hoffmann, “Ff: The fast-forward planning system,” AI magazine , vol. 22, no. 3, pp. 57–57, 2001
2001
Earlier work this paper cites.
A. G. Barto and S. Mahadevan, “Recent advances in hierarchical reinforcement learning,” Discrete event dynamic systems , vol. 13, no. 1-2, pp. 41–77, 2003
2003
Earlier work this paper cites.
M. Helmert, “The fast downward planning system,” Journal of Artificial Intelligence Research , vol. 26, pp. 191–246, 2006
2006
Earlier work this paper cites.
C. B. Browne, E. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S. Samothrakis, and S. Colton, “A survey of monte carlo tree search methods,” IEEE Transactions on Computational Intelligence and AI in games , vol. 4, no. 1, pp. 1–43, 2012
2012
Earlier work this paper cites.
P. Benavidez, M. Kumar, S. Agaian, and M. Jamshidi, “Design of a home multi-robot system for the elderly and disabled,” in 2015 10th System of Systems Engineering Conference (SoSE) . IEEE, 2015, pp. 392–397
2015
Earlier work this paper cites.
O. Nachum, S. S. Gu, H. Lee, and S. Levine, “Data-efficient hierarchical reinforcement learning,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
X. Puig, K. Ra, M. Boben, J. Li, T. Wang, S. Fidler, and A. Torralba, “Virtualhome: Simulating household activities via programs,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 8494–8502
2018
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
J. P. Queralta, J. Taipalmaa, B. C. Pullinen, V. K. Sarker, T. N. Gia, H. Tenhunen, M. Gabbouj, J. Raitoharju, and T. Westerlund, “Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision,” Ieee Access , vol. 8, pp. 191 617–191 643, 2020
2020
Earlier work this paper cites.
C. R. Garrett, T. Lozano-Pérez, and L. P. Kaelbling, “Pddlstream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning,” in Proceedings of the International Conference on Automated Planning and Scheduling , vol. 30, 2020, pp. 440–448
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
S. Pateria, B. Subagdja, A.-h. Tan, and C. Quek, “Hierarchical reinforcement learning: A comprehensive survey,” ACM Computing Surveys (CSUR) , vol. 54, no. 5, pp. 1–35, 2021
2021
Earlier work this paper cites.
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” Advances in neural information processing systems , vol. 35, pp. 22 199–22 213, 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
Cited alongside, same era.
C. Ju, J. Kim, J. Seol, and H. I. Son, “A review on multirobot systems in agriculture,” Computers and Electronics in Agriculture , vol. 202, p. 107336, 2022
2022
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.
A. Brohan, Y. Chebotar, C. Finn, K. Hausman, A. Herzog, D. Ho, J. Ibarz, A. Irpan, E. Jang, R. Julian et al. , “Do as i can, not as i say: Grounding language in robotic affordances,” in Conference on Robot Learning . PMLR, 2023, pp. 287–318
2023
Later among the works it cites.
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…
R. Gong, X. Gao, Q. Gao, S. Shakiah, G. Thattai, and G. S. Sukhatme, “Lemma: Learning language-conditioned multi-robot manipulation,” IEEE Robotics and Automation Letters , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
H. R. Walke, K. Black, T. Z. Zhao, Q. Vuong, C. Zheng, P. Hansen-Estruch, A. W. He, V. Myers, M. J. Kim, M. Du et al. , “Bridgedata v2: A dataset for robot learning at scale,” in Conference on Robot Learning . PMLR, 2023, pp. 1723–1736
2023
Cited alongside, same era.
2023
Cited alongside, same era.
W. Huang, C. Wang, R. Zhang, Y. Li, J. Wu, and L. Fei-Fei, “Voxposer: Composable 3d value maps for robotic manipulation with language models,” in Conference on Robot Learning . PMLR, 2023, pp. 540–562
2023
Cited alongside, same era.
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,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 9493–9500
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.
C. Li, R. Zhang, J. Wong, C. Gokmen, S. Srivastava, R. Martín-Martín, C. Wang, G. Levine, M. Lingelbach, J. Sun et al. , “Behavior-1k: A benchmark for embodied ai with 1,000 everyday activities and realistic simulation,” in Conference on Robot Learning . PMLR, 2023, pp. 80–93
2023
Later among the works it cites.
2023
Later among the works it cites.
W. Hunt, T. Godfrey, and M. D. Soorati, “Conversational language models for human-in-the-loop multi-robot coordination,” in Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems , 2024, pp. 2809–2811
2024
Closest in time.
Z. Mandi, S. Jain, and S. Song, “Roco: Dialectic multi-robot collaboration with large language models,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 286–299
2024
Closest in time.
2024
Closest in time.
Y. Chen, J. Arkin, Y. Zhang, N. Roy, and C. Fan, “Scalable multi-robot collaboration with large language models: Centralized or decentralized systems?” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 4311–4317
2024
Closest in time.