Fetching the paper…
Reading the bibliography…
For effective human-robot interaction, robots need to understand, plan, and execute complex, long-horizon tasks described by natural language.
R. E. Fikes and N. J. Nilsson, “Strips: A new approach to the application of theorem proving to problem solving,” Artificial Intelligence , vol. 2, no. 3, pp. 189–208, 1971. [Online]. Available: https://www.sciencedirect.com/science/article/pii/0004370271900105
1971
Earlier work this paper cites.
E. A. Emerson, “Temporal and modal logic,” in Formal Models and Semantics . Elsevier, 1990, pp. 995–1072
1990
Earlier work this paper cites.
C. Finucane, G. Jing, and H. Kress-Gazit, “Ltlmop: Experimenting with language, temporal logic and robot control,” in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2010, pp. 1988–1993
1993
Earlier work this paper cites.
M. Fox and D. Long, “Pddl2. 1: An extension to pddl for expressing temporal planning domains,” Journal of artificial intelligence research , vol. 20, pp. 61–124, 2003
2003
Earlier work this paper cites.
O. Maler and D. Nickovic, “Monitoring temporal properties of continuous signals,” in Formal Techniques, Modelling and Analysis of Timed and Fault-Tolerant Systems: Joint International Conferences on Formal Modeling and Analysis of Timed Systmes, FORMATS 2004, and Formal Techniques in Real-Time and Fault-Tolerant Systems, FTRTFT 2004, Grenoble, France, September 22-24, 2004. Proceedings . Springer, 2004, pp. 152–166
2004
Earlier work this paper cites.
L. S. Zettlemoyer and M. Collins, “Learning to map sentences to logical form: structured classification with probabilistic categorial grammars,” in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence , 2005, pp. 658–666
2005
Earlier work this paper cites.
S. M. LaValle, Planning algorithms . Cambridge university press, 2006
2006
Earlier work this paper cites.
Y. W. Wong and R. J. Mooney, “Learning for semantic parsing with statistical machine translation,” in Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics . Association for Computational Linguistics, 2006, pp. 439–446
2006
Earlier work this paper cites.
L. Zettlemoyer and M. Collins, “Online learning of relaxed ccg grammars for parsing to logical form,” in Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) , 2007, pp. 678–687
2007
Earlier work this paper cites.
H. Kress-Gazit, G. E. Fainekos, and G. J. Pappas, “Translating structured english to robot controllers,” Advanced Robotics , vol. 22, no. 12, pp. 1343–1359, 2008
2008
Earlier work this paper cites.
J. Dzifcak, M. Scheutz, C. Baral, and P. Schermerhorn, “What to do and how to do it: Translating natural language directives into temporal and dynamic logic representation for goal management and action execution,” in 2009 IEEE International Conference on Robotics and Automation . IEEE, 2009, pp. 4163–4168
2009
Earlier work this paper cites.
J. Wolfe, B. Marthi, and S. Russell, “Combined task and motion planning for mobile manipulation,” in Proceedings of the International Conference on Automated Planning and Scheduling , vol. 20, 2010, pp. 254–257
2010
Earlier work this paper cites.
L. P. Kaelbling and T. Lozano-Pérez, “Integrated task and motion planning in belief space,” The International Journal of Robotics Research , vol. 32, no. 9-10, pp. 1194–1227, 2013
2013
Earlier work this paper cites.
Y. Artzi and L. Zettlemoyer, “Weakly supervised learning of semantic parsers for mapping instructions to actions,” Transactions of the Association for Computational Linguistics , vol. 1, pp. 49–62, 2013
2013
Earlier work this paper cites.
S. Srivastava, E. Fang, L. Riano, R. Chitnis, S. Russell, and P. Abbeel, “Combined task and motion planning through an extensible planner-independent interface layer,” in 2014 IEEE international conference on robotics and automation (ICRA) . IEEE, 2014, pp. 639–646
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.
K. He, M. Lahijanian, L. E. Kavraki, and M. Y. Vardi, “Towards manipulation planning with temporal logic specifications,” in 2015 IEEE International Conference on Robotics and Automation (ICRA) , 2015, pp. 346–352
2015
Earlier work this paper cites.
K. He, M. Lahijanian, L. E. Kavraki, and M. Y. Vardi, “Towards manipulation planning with temporal logic specifications,” in 2015 IEEE international conference on robotics and automation (ICRA) . IEEE, 2015, pp. 346–352
2015
Earlier work this paper cites.
A. Akbari, J. Rosell, et al. , “Task planning using physics-based heuristics on manipulation actions,” in 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) . IEEE, 2016, pp. 1–8
2016
Earlier work this paper cites.
F. Lagriffoul and B. Andres, “Combining task and motion planning: A culprit detection problem,” The International Journal of Robotics Research , vol. 35, no. 8, pp. 890–927, 2016
2016
Earlier work this paper cites.
A. Boteanu, J. Arkin, T. Howard, and H. Kress-Gazit, “A model for verifiable grounding and execution of complex language instructions,” in Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems , Oct. 2016
2016
Cited alongside, same era.
2017
Cited alongside, same era.
C. R. Garrett, T. Lozano-Perez, and L. P. Kaelbling, “Ffrob: Leveraging symbolic planning for efficient task and motion planning,” The International Journal of Robotics Research , vol. 37, no. 1, pp. 104–136, 2018
2018
Cited alongside, same era.
E. Fernandez-Gonzalez, B. Williams, and E. Karpas, “Scottyactivity: Mixed discrete-continuous planning with convex optimization,” Journal of Artificial Intelligence Research , vol. 62, pp. 579–664, 2018
2018
Cited alongside, same era.
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” in ICML 2022 Workshop on Knowledge Retrieval and Language Models , 2022. [Online]. Available: https://openreview.net/forum?id=6p3AuaHAFiN
2022
Later among the works it cites.
T. Silver, V. Hariprasad, R. S. Shuttleworth, N. Kumar, T. Lozano-Pérez, and L. P. Kaelbling, “PDDL planning with pretrained large language models,” in NeurIPS 2022 Foundation Models for Decision Making Workshop , 2022. [Online]. Available: https://openreview.net/forum?id=1QMMUB4zfl
2022
Later among the works it cites.
J. He, E. Bartocci, D. Ničković, H. Isakovic, and R. Grosu, “Deepstl: from english requirements to signal temporal logic,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 610–622
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
N. Gopalan, D. Arumugam, L. L. Wong, and S. Tellex, “Sequence-to-sequence language grounding of non-markovian task specifications.” in Robotics: Science and Systems , vol. 2018, 2018
2018
Cited alongside, same era.
M. Colledanchise, D. Almeida, and P. Ögren, “Towards blended reactive planning and acting using behavior trees,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 8839–8845
2019
Cited alongside, same era.
C. N. Bonial, L. Donatelli, J. Ervin, and C. R. Voss, “Abstract meaning representation for human-robot dialogue,” Proceedings of the Society for Computation in Linguistics , vol. 2, no. 1, pp. 236–246, 2019
2019
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
S. Wilson, P. Glotfelter, L. Wang, S. Mayya, G. Notomista, M. Mote, and M. Egerstedt, “The robotarium: Globally impactful opportunities, challenges, and lessons learned in remote-access, distributed control of multirobot systems,” IEEE Control Systems Magazine , vol. 40, no. 1, pp. 26–44, 2020
2020
Cited alongside, same era.
M. Katayama, S. Tokuda, M. Yamakita, and H. Oyama, “Fast ltl-based flexible planning for dual-arm manipulation,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 6605–6612
2020
Cited alongside, same era.
R. Patel, E. Pavlick, and S. Tellex, “Grounding language to non-markovian tasks with no supervision of task specifications.” in Robotics: Science and Systems , vol. 2020, 2020
2020
Cited alongside, same era.
2022
Later among the works it cites.
J. X. Liu, Z. Yang, B. Schornstein, S. Liang, I. Idrees, S. Tellex, and A. Shah, “Lang2LTL: Translating natural language commands to temporal specification with large language models,” in Workshop on Language and Robotics at CoRL 2022 , 2022. [Online]. Available: https://openreview.net/forum?id=VxfjGZzrdn
2022
Later among the works it cites.
2022
Later among the works it 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. [Online]. Available: https://openreview.net/forum?id=cMDMRBe1TKs
2022
Later among the works it cites.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.