Fetching the paper…
Reading the bibliography…
This paper addresses task planning problems for language-instructed robot teams.
L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars, “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE transactions on Robotics and Automation , vol. 12, no. 4, pp. 566–580, 1996
1996
Earlier work this paper cites.
Y. W. Wong and R. Mooney, “Learning for semantic parsing with statistical machine translation,” in Proceedings of the Human Language Technology Conference of the NAACL, Main Conference , 2006, pp. 439–446
2006
Earlier work this paper cites.
C. Baier and J.-P. Katoen, Principles of model checking . MIT press Cambridge, 2008, vol. 26202649
2008
Earlier work this paper cites.
G. Shafer and V. Vovk, “A tutorial on conformal prediction.” Journal of Machine Learning Research , vol. 9, no. 3, 2008
2008
Earlier work this paper cites.
P. Koehn, Statistical machine translation . Cambridge University Press, 2009
2009
Earlier work this paper cites.
C. Matuszek, D. Fox, and K. Koscher, “Following directions using statistical machine translation,” in 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI) . IEEE, 2010, pp. 251–258
2010
Earlier work this paper cites.
T. Kollar, S. Tellex, D. Roy, and N. Roy, “Toward understanding natural language directions,” in 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI) . IEEE, 2010, pp. 259–266
2010
Earlier work this paper cites.
S. Karaman and E. Frazzoli, “Sampling-based algorithms for optimal motion planning,” The International Journal of Robotics Research , vol. 30, no. 7, pp. 846–894, 2011
2011
Earlier work this paper cites.
S. Tellex, T. Kollar, S. Dickerson, M. Walter, A. Banerjee, S. Teller, and N. Roy, “Understanding natural language commands for robotic navigation and mobile manipulation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 25, no. 1, 2011, pp. 1507–1514
2011
Earlier work this paper cites.
D. Chen and R. Mooney, “Learning to interpret natural language navigation instructions from observations,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 25, no. 1, 2011, pp. 859–865
2011
Earlier work this paper cites.
V. Vovk, “Conditional validity of inductive conformal predictors,” in Asian conference on machine learning . PMLR, 2012, pp. 475–490
2012
Earlier work this paper cites.
M. Turpin, N. Michael, and V. Kumar, “Capt: Concurrent assignment and planning of trajectories for multiple robots,” The International Journal of Robotics Research , vol. 33, no. 1, pp. 98–112, 2014
2014
Earlier work this paper cites.
T. M. Howard, S. Tellex, and N. Roy, “A natural language planner interface for mobile manipulators,” in 2014 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2014, pp. 6652–6659
2014
Earlier work this paper cites.
V. Balasubramanian, S.-S. Ho, and V. Vovk, Conformal prediction for reliable machine learning: theory, adaptations and applications . Newnes, 2014
2014
Earlier work this paper cites.
Y. Kantaros and M. M. Zavlanos, “Distributed communication-aware coverage control by mobile sensor networks,” Automatica , vol. 63, pp. 209–220, 2016
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
B. Schlotfeldt, D. Thakur, N. Atanasov, V. Kumar, and G. J. Pappas, “Anytime planning for decentralized multirobot active information gathering,” IEEE Robotics and Automation Letters , vol. 3, no. 2, pp. 1025–1032, 2018
2018
Earlier work this paper cites.
V. Vasilopoulos and D. E. Koditschek, “Reactive navigation in partially known non-convex environments,” in International Workshop on the Algorithmic Foundations of Robotics . Springer, 2018, pp. 406–421
2018
Earlier work this paper cites.
R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction . MIT press, 2018
2018
Earlier work this paper cites.
P. Schillinger, M. Bürger, and D. V. Dimarogonas, “Decomposition of finite ltl specifications for efficient multi-agent planning,” in Distributed Autonomous Robotic Systems . Springer, 2018, pp. 253–267
2018
Earlier work this paper cites.
P. Pianpak, T. C. Son, P. O. Toups Dugas, and W. Yeoh, “A distributed solver for multi-agent path finding problems,” in Proceedings of the First International Conference on Distributed Artificial Intelligence , 2019, pp. 1–7
2019
Earlier work this paper cites.
Y. Xiao and W. Y. Wang, “Quantifying uncertainties in natural language processing tasks,” in Proceedings of the AAAI conference on artificial intelligence , vol. 33, no. 01, 2019, pp. 7322–7329
2019
Earlier work this paper cites.
M. Sadinle, J. Lei, and L. Wasserman, “Least ambiguous set-valued classifiers with bounded error levels,” Journal of the American Statistical Association , vol. 114, no. 525, pp. 223–234, 2019
2019
Earlier work this paper cites.
Y. Kantaros and M. M. Zavlanos, “Stylus*: A temporal logic optimal control synthesis algorithm for large-scale multi-robot systems,” The International Journal of Robotics Research , vol. 39, no. 7, pp. 812–836, 2020
2020
Earlier work this paper cites.
S. Stepputtis, J. Campbell, M. Phielipp, S. Lee, C. Baral, and H. Ben Amor, “Language-conditioned imitation learning for robot manipulation tasks,” Advances in Neural Information Processing Systems , vol. 33, pp. 13 139–13 150, 2020
2020
Earlier work this paper cites.
C. R. Garrett, R. Chitnis, R. Holladay, B. Kim, T. Silver, L. P. Kaelbling, and T. Lozano-Pérez, “Integrated task and motion planning,” Annual review of control, robotics, and autonomous systems , 2021
2021
Earlier work this paper cites.
A. Angelopoulos, S. Bates, J. Malik, and M. I. Jordan, “Uncertainty sets for image classifiers using conformal prediction,” International Conference on Learning Representations (ICLR) , 2021
2021
Earlier work this paper cites.
W. Gosrich, S. Mayya, R. Li, J. Paulos, M. Yim, A. Ribeiro, and V. Kumar, “Coverage control in multi-robot systems via graph neural networks,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 8787–8793
2022
Earlier work this paper cites.
K. Elimelech, L. E. Kavraki, and M. Y. Vardi, “Efficient task planning using abstract skills and dynamic road map matching,” in The International Symposium of Robotics Research . Springer, 2022, pp. 487–503
2022
Cited alongside, same era.
A. Fang and H. Kress-Gazit, “Automated task updates of temporal logic specifications for heterogeneous robots,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 4363–4369
2022
Cited alongside, same era.
2022
Cited alongside, same era.
S. Li, X. Puig, C. Paxton, Y. Du, C. Wang, L. Fan, T. Chen, D.-A. Huang, E. Akyürek, A. Anandkumar et al. , “Pre-trained language models for interactive decision-making,” Advances in Neural Information Processing Systems , vol. 35, pp. 31 199–31 212, 2022
2022
2023
Later among the works it cites.
A. N. Angelopoulos, S. Bates et al. , “Conformal prediction: A gentle introduction,” Foundations and Trends® in Machine Learning , vol. 16, no. 4, pp. 494–591, 2023
2023
Later among the works it cites.
B. Kumar, C. Lu, G. Gupta, A. Palepu, D. Bellamy, R. Raskar, and A. Beam, “Conformal prediction with large language models for multi-choice question answering,” in ICML (Neural Conversational AI TEACH) workshop , 2023
2023
Later among the works it cites.
A. Z. Ren, A. Dixit, A. Bodrova, S. Singh, S. Tu, N. Brown, P. Xu, L. Takayama, F. Xia, J. Varley, Z. Xu, D. Sadigh, A. Zeng, and A. Majumdar, “Robots that ask for help: Uncertainty alignment for large language model planners,” Conference on Robot Learning , 2023
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…
Cited alongside, same era.
W. Huang, F. Xia, T. Xiao, H. Chan, J. Liang, P. Florence, A. Zeng, J. Tompson, I. Mordatch, Y. Chebotar et al. , “Inner monologue: Embodied reasoning through planning with language models,” Conference on Robot Learning (CoRL) , 2022
2022
Cited alongside, same era.
M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakrishnan, K. Hausman et al. , “Do as i can, not as i say: Grounding language in robotic affordances,” Conference on Robot Learning (CoRL) , 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
V. Manokhin, “Awesome conformal prediction,” Apr. 2022. [Online]. Available: https://doi.org/10.5281/zenodo.6467205
2022
Cited alongside, same era.
L. Antonyshyn, J. Silveira, S. Givigi, and J. Marshall, “Multiple mobile robot task and motion planning: A survey,” ACM Computing Surveys , vol. 55, no. 10, pp. 1–35, 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.
H. Yang and M. Pavone, “Object pose estimation with statistical guarantees: Conformal keypoint detection and geometric uncertainty propagation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 8947–8958
2023
Later among the works it cites.
L. Lindemann, M. Cleaveland, G. Shim, and G. J. Pappas, “Safe planning in dynamic environments using conformal prediction,” IEEE Robotics and Automation Letters , 2023
2023
Later among the works it cites.
J. Sun, Y. Jiang, J. Qiu, P. T. Nobel, M. Kochenderfer, and M. Schwager, “Conformal prediction for uncertainty-aware planning with diffusion dynamics model,” in Thirty-seventh Conference on Neural Information Processing Systems , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Chen, Z. Zhou, S. Wang, J. Li, and Z. Kan, “Fast temporal logic mission planning of multiple robots: A planning decision tree approach,” IEEE Robotics and Automation Letters , 2024
2024
Closest in time.
Z. Yang, L. Ning, H. Wang, T. Jiang, S. Zhang, S. Cui, H. Jiang, C. Li, S. Wang, and Z. Wang, “Text2reaction: Enabling reactive task planning using large language models,” IEEE Robotics and Automation Letters , 2024
2024
Closest in time.
2024
Closest in time.
Z. Mandi, S. Jain, and S. Song, “Roco: Dialectic multi-robot collaboration with large language models,” IEEE International Conference on Robotics and Automation (ICRA) , 2024
2024
Closest in time.
H. Zhang, W. Du, J. Shan, Q. Zhou, Y. Du, J. B. Tenenbaum, T. Shu, and C. Gan, “Building cooperative embodied agents modularly with large language models,” International Conference on Learning Representations (ICLR) , 2024
2024
Closest in time.
Z. Liu, W. Yao, J. Zhang, L. Xue, S. Heinecke, R. Murthy, Y. Feng, Z. Chen, J. C. Niebles, D. Arpit et al. , “Bolaa: Benchmarking and orchestrating llm-augmented autonomous agents,” in ICLR Workshop on LLM Agents , 2024
2024
Closest in time.
S. Hong, X. Zheng, J. Chen, Y. Cheng, J. Wang, C. Zhang, Z. Wang, S. K. S. Yau, Z. Lin, L. Zhou et al. , “Metagpt: Meta programming for multi-agent collaborative framework,” International Conference on Learning Representations , 2024
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?” International Conference on Robotics and Automation , 2024
2024
Closest in time.
B. Zhang, H. Mao, J. Ruan, Y. Wen, Y. Li, S. Zhang, Z. Xu, D. Li, Z. Li, R. Zhao et al. , “Controlling large language model-based agents for large-scale decision-making: An actor-critic approach,” in International Conference on Learning Representations , 2024
2024
Closest in time.
2024
Closest in time.
X. Liu, P. Li, W. Yang, D. Guo, and H. Liu, “Leveraging large language model for heterogeneous ad hoc teamwork collaboration,” in Robotics: Science and Systems , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Z. Mao, C. Sobolewski, and I. Ruchkin, “How safe am i given what i see? calibrated prediction of safety chances for image-controlled autonomy,” in Proceedings of the 6th Annual Learning for Dynamics and Control Conference , 2024
2024
Closest in time.
S. Su, S. Han, Y. Li, Z. Zhang, C. Feng, C. Ding, and F. Miao, “Collaborative multi-object tracking with conformal uncertainty propagation,” IEEE Robotics and Automation Letters , 2024
2024
Closest in time.
M. Cleaveland, I. Lee, G. J. Pappas, and L. Lindemann, “Conformal prediction regions for time series using linear complementarity programming,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 19, 2024, pp. 20 984–20 992
2024
Closest in time.
A. Dixit, Z. Mei, M. Booker, M. Storey-Matsutani, A. Z. Ren, and A. Majumdar, “Perceive with confidence: Statistical safety assurances for navigation with learning-based perception,” in 8th Annual Conference on Robot Learning , 2024
2024
Closest in time.
M. Cauchois, S. Gupta, A. Ali, and J. C. Duchi, “Robust validation: Confident predictions even when distributions shift,” Journal of the American Statistical Association , pp. 1–66, 2024
2024
Closest in time.