Fetching the paper…
Reading the bibliography…
Bimanual robotic manipulation provides significant versatility, but also presents an inherent challenge due to the complexity involved in the spatial and temporal coordination between two hands.
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. 3-4, pp. 189–208, 1971
1971
Earlier work this paper cites.
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.
V. Lifschitz, “Answer set programming and plan generation,” Artificial Intelligence , vol. 138, no. 1-2, pp. 39–54, 2002
2002
Earlier work this paper cites.
M. Ghallab, D. Nau, and P. Traverso, Automated Planning: theory and practice . Elsevier, 2004
2004
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.
J. Cox and E. Durfee, “Efficient and distributable methods for solving the multiagent plan coordination problem,” Multiagent and Grid Systems , vol. 5, no. 4, pp. 373–408, 2009
2009
Earlier work this paper cites.
C. Smith, Y. Karayiannidis, L. Nalpantidis, X. Gratal, P. Qi, D. V. Dimarogonas, and D. Kragic, “Dual arm manipulation—a survey,” Robotics and Autonomous systems , vol. 60, no. 10, pp. 1340–1353, 2012
2012
Earlier work this paper cites.
D. L. Kovacs et al. , “A multi-agent extension of PDDL3.1,” in ICAPS 2012 Proceedings of the 3rd Workshop on the International Planning Competition (WS-IPC 2012) , 2012, pp. 19–37
2012
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.
A. Torreno, E. Onaindia, and O. Sapena, “FMAP: Distributed cooperative multi-agent planning,” Applied Intelligence , vol. 41, pp. 606–626, 2014
2014
Earlier work this paper cites.
G. Frances, H. Geffner, N. Lipovetzky, and M. Ramiréz, “Best-first width search in the IPC 2018: Complete, simulated, and polynomial variants,” IPC-9 Planner Abstracts , pp. 23–27, 2018
2018
Earlier work this paper cites.
Y.-q. Jiang, S.-q. Zhang, P. Khandelwal, and P. Stone, “Task planning in robotics: an empirical comparison of PDDL-and ASP-based systems,” Frontiers of Information Technology & Electronic Engineering , vol. 20, pp. 363–373, 2019
2019
Earlier work this paper cites.
Y. Jiang, H. Yedidsion, S. Zhang, G. Sharon, and P. Stone, “Multi-robot planning with conflicts and synergies,” Autonomous Robots , vol. 43, no. 8, pp. 2011–2032, 2019
2019
Earlier work this paper cites.
A. E. Gerevini, “An introduction to the planning domain definition language (PDDL): Book review,” Artificial Intelligence , vol. 280, p. 103221, 2020
2020
Earlier work this paper cites.
Y. Ding, X. Zhang, X. Zhan, and S. Zhang, “Task-motion planning for safe and efficient urban driving,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 2119–2125
2020
Earlier work this paper cites.
Z. Jiao, Z. Zhang, W. Wang, D. Han, S.-C. Zhu, Y. Zhu, and H. Liu, “Efficient task planning for mobile manipulation: a virtual kinematic chain perspective,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 8288–8294
2021
Earlier work this paper cites.
F. Krebs and T. Asfour, “A bimanual manipulation taxonomy,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 11 031–11 038, 2022
2022
Cited alongside, same era.
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 (ICML) . PMLR, 2022, pp. 9118–9147
2022
Cited alongside, same era.
Y. Chen, T. Wu, S. Wang, X. Feng, J. Jiang, Z. Lu, S. McAleer, H. Dong, S.-C. Zhu, and Y. Yang, “Towards human-level bimanual dexterous manipulation with reinforcement learning,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 35, pp. 5150–5163, 2022
2022
Cited alongside, same era.
Y. Avigal, L. Berscheid, T. Asfour, T. Kröger, and K. Goldberg, “Speedfolding: Learning efficient bimanual folding of garments,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 1–8
2022
Cited alongside, same era.
K. Chu, X. Zhao, C. Weber, M. Li, W. Lu, and S. Wermter, “Large language models for orchestrating bimanual robots,” in 2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids) , 2024, pp. 328–334
2024
Later among the works it cites.
2024
Later among the works it cites.
L. Wang, C. Ma, X. Feng, Z. Zhang, H. Yang, J. Zhang, Z. Chen, J. Tang, X. Chen, Y. Lin, et al. , “A survey on large language model based autonomous agents,” Frontiers of Computer Science , vol. 18, no. 6, p. 186345, 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Grannen, Y. Wu, B. Vu, and D. Sadigh, “Stabilize to act: Learning to coordinate for bimanual manipulation,” in The 7th Conference on Robot Learning (CoRL) . PMLR, 2023, pp. 563–576
2023
Cited alongside, same era.
J. Grannen, Y. Wu, S. Belkhale, and D. Sadigh, “Learning bimanual scooping policies for food acquisition,” in Proceedings of The 6th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, K. Liu, D. Kulic, and J. Ichnowski, Eds., vol. 205. PMLR, 14–18 Dec 2023, pp. 1510–1519. [Online]. Available: https://proceedings.mlr.press/v205/grannen23a.html
2023
Cited alongside, same era.
2023
Cited alongside, same era.
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 Proceedings of The 6th Conference on Robot Learning (CoRL) . PMLR, 2023, pp. 287–318
2023
Cited alongside, same era.
X. Zhao, M. Li, C. Weber, M. B. Hafez, and S. Wermter, “Chat with the environment: Interactive multimodal perception using large language models,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2023, pp. 3590–3596
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
M. Kerzel, P. Allgeuer, E. Strahl, N. Frick, J.-G. Habekost, M. Eppe, and S. Wermter, “NICOL: A neuro-inspired collaborative semi-humanoid robot that bridges social interaction and reliable manipulation,” IEEE Access , vol. 11, pp. 123 531–123 542, 2023
2023
Cited alongside, same era.
X. Zhao, M. Li, W. Lu, C. Weber, J. H. Lee, K. Chu, and S. Wermter, “Enhancing zero-shot chain-of-thought reasoning in large language models through logic,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) . ELRA and ICCL, May 2024, pp. 6144–6166
2024
Later among the works it cites.
2024
Later among the works it cites.
K. Rana, J. Haviland, S. Garg, J. Abou-Chakra, I. Reid, and N. Suenderhauf, “SayPlan: Grounding large language models using 3D scene graphs for scalable robot task planning,” in 7th Annual Conference on Robot Learning (CoRL) , 2024
2024
Later among the works it cites.
Y. Chen, J. Arkin, C. Dawson, Y. Zhang, N. Roy, and C. Fan, “AutoTAMP: Autoregressive task and motion planning with LLMs as translators and checkers,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 6695–6702
2024
Later among the works it cites.
S. Hong, M. Zhuge, J. Chen, X. Zheng, Y. Cheng, J. Wang, C. Zhang, Z. Wang, S. K. S. Yau, Z. Lin, L. Zhou, C. Ran, L. Xiao, C. Wu, and J. Schmidhuber, “MetaGPT: Meta programming for a multi-agent collaborative framework,” in The Twelfth International Conference on Learning Representations (ICLR) , 2024
2024
Later among the works it cites.
S. S. Kannan, V. L. N. Venkatesh, and B.-C. Min, “SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Oct. 2024, pp. 12 140–12 147
2024
Later among the works it cites.
2024
Later among the works it cites.
2025
Closest in time.
A. Micheli, A. Bit-Monnot, G. Röger, E. Scala, A. Valentini, L. Framba, A. Rovetta, A. Trapasso, L. Bonassi, A. E. Gerevini, L. Iocchi, F. Ingrand, U. Köckemann, F. Patrizi, A. Saetti, I. Serina, and S. Stock, “Unified planning: Modeling, manipulating and solving AI planning problems in python,” SoftwareX , vol. 29, p. 102012, 2025. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2352711024003820
2025
Closest in time.
K. Shirai, C. C. Beltran-Hernandez, M. Hamaya, A. Hashimoto, S. Tanaka, K. Kawaharazuka, K. Tanaka, Y. Ushiku, and S. Mori, “Vision-language interpreter for robot task planning,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 2051–2058
2058
Closest in time.
Y. Ding, X. Zhang, C. Paxton, and S. Zhang, “Task and motion planning with large language models for object rearrangement,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 2086–2092
2092
Closest in time.