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Task and motion planning (TAMP) algorithms aim to help robots achieve task-level goals, while maintaining motion-level feasibility.
M. Toussaint, “Logic-geometric programming: an optimization-based approach to combined task and motion planning,” in Proceedings of the 24th International Conference on Artificial Intelligence , 2015, pp. 1930–1936
1936
Earlier work this paper cites.
S. Harnad, “The symbol grounding problem,” Physica D: Nonlinear Phenomena , vol. 42, no. 1-3, pp. 335–346, 1990
1990
Earlier work this paper cites.
S. M. LaValle et al. , “Rapidly-exploring random trees: A new tool for path planning,” 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.
N. Koenig and A. Howard, “Design and use paradigms for gazebo, an open-source multi-robot simulator,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2004
2004
Earlier work this paper cites.
H. M. Choset, K. M. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. Kavraki, S. Thrun, and R. C. Arkin, Principles of robot motion: theory, algorithms, and implementation , 2005
2005
Earlier work this paper cites.
F. Gravot, S. Cambon, and R. Alami, “asymov: a planner that deals with intricate symbolic and geometric problems,” in The Eleventh International Symposium of Robotics Research , 2005
2005
Earlier work this paper cites.
E. Plaku, L. E. Kavraki, and M. Y. Vardi, “Discrete search leading continuous exploration for kinodynamic motion planning.” in Robotics: Science and Systems , 2007, pp. 326–333
2007
Earlier work this paper cites.
M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, A. Y. Ng et al. , “Ros: an open-source robot operating system,” in ICRA workshop on open source software , vol. 3, no. 3.2. Kobe, Japan, 2009, p. 5
2009
Earlier work this paper cites.
E. Erdem, K. Haspalamutgil, C. Palaz, V. Patoglu, and T. Uras, “Combining high-level causal reasoning with low-level geometric reasoning and motion planning for robotic manipulation,” in IEEE International Conference on Robotics and Automation , 2011
2011
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.
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.
F. Lagriffoul, D. Dimitrov, J. Bidot, A. Saffiotti, and L. Karlsson, “Efficiently combining task and motion planning using geometric constraints,” The International Journal of Robotics Research , vol. 33, no. 14, pp. 1726–1747, 2014
2014
Earlier work this paper cites.
M. Gelfond and Y. Kahl, Knowledge representation, reasoning, and the design of intelligent agents: The answer-set programming approach . Cambridge University Press, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3431–3440
2015
Cited alongside, same era.
D. Hadfield-Menell, E. Groshev, R. Chitnis, and P. Abbeel, “Modular task and motion planning in belief space,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2015
2015
Cited alongside, same era.
M. Ghallab, D. Nau, and P. Traverso, Automated planning and acting . Cambridge University Press, 2016
2016
Cited alongside, same era.
R. Chitnis, D. Hadfield-Menell, A. Gupta, S. Srivastava, E. Groshev, C. Lin, and P. Abbeel, “Guided search for task and motion plans using learned heuristics,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2016, pp. 447–454
2016
Cited alongside, same era.
C. Phiquepal and M. Toussaint, “Combined task and motion planning under partial observability: An optimization-based approach,” in IEEE International Conference on Robotics and Automation (ICRA) , 2019
2019
Later among the works it cites.
Y. Zhu, J. Tremblay, S. Birchfield, and Y. Zhu, “Hierarchical planning for long-horizon manipulation with geometric and symbolic scene graphs,” 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2020
2020
Later among the works it cites.
T. Migimatsu and J. Bohg, “Object-centric task and motion planning in dynamic environments,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 844–851, 2020
2020
Later among the works it cites.
S.-Y. Lo, S. Zhang, and P. Stone, “The petlon algorithm to plan efficiently for task-level-optimal navigation,” Journal of Artificial Intelligence Research , vol. 69, pp. 471–500, 2020
2020
Later among the works it cites.
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J. McMahon and E. Plaku, “Robot motion planning with task specifications via regular languages,” Robotica , 2017
2017
Cited alongside, same era.
P. Khandelwal, S. Zhang, J. Sinapov, M. Leonetti, J. Thomason, F. Yang, I. Gori, M. Svetlik, P. Khante, V. Lifschitz et al. , “Bwibots: A platform for bridging the gap between ai and human–robot interaction research,” The International Journal of Robotics Research , vol. 36, no. 5-7, pp. 635–659, 2017
2017
Cited alongside, same era.
F. Lagriffoul, N. T. Dantam, C. Garrett, A. Akbari, S. Srivastava, and L. E. Kavraki, “Platform-independent benchmarks for task and motion planning,” IEEE Robotics and Automation Letters , vol. 3, no. 4, pp. 3765–3772, 2018
2018
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.
2018
Cited alongside, same era.
Z. Wang, C. R. Garrett, L. P. Kaelbling, and T. Lozano-Pérez, “Active model learning and diverse action sampling for task and motion planning,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 4107–4114
2018
Cited alongside, same era.
N. T. Dantam, Z. K. Kingston, S. Chaudhuri, and L. E. Kavraki, “An incremental constraint-based framework for task and motion planning,” The International Journal of Robotics Research , vol. 37, no. 10, pp. 1134–1151, 2018
2018
Cited alongside, same era.
Y. Jiang, F. Yang, S. Zhang, and P. Stone, “Task-motion planning with reinforcement learning for adaptable mobile service robots,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2019
2019
Cited alongside, same era.
D. Driess, O. Oguz, J.-S. Ha, and M. Toussaint, “Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning,” in IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 9563–9569
2020
Later among the works it cites.
B. Kim and L. Shimanuki, “Learning value functions with relational state representations for guiding task-and-motion planning,” in Conference on Robot Learning . PMLR, 2020, pp. 955–968
2020
Later among the works it cites.
Y. Ding, X. Zhang, X. Zhan, and S. Zhang, “Task-motion planning for safe and efficient urban driving,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020
2020
Later among the works it cites.
C. R. Garrett, C. Paxton, T. Lozano-Pérez, L. P. Kaelbling, and D. Fox, “Online replanning in belief space for partially observable task and motion problems,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 5678–5684
2020
Later among the works it cites.
A. Akbari, M. Diab, and J. Rosell, “Contingent task and motion planning under uncertainty for human–robot interactions,” Applied Sciences , vol. 10, no. 5, p. 1665, 2020
2020
Later among the works it cites.
D. Driess, J.-S. Ha, and M. Toussaint, “Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image,” in Proceedings of Robotics: Science and Systems , 2020
2020
Later among the works it 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 , vol. 4, pp. 265–293, 2021
2021
Later among the works it cites.
A. Thomas, F. Mastrogiovanni, and M. Baglietto, “Mptp: Motion-planning-aware task planning for navigation in belief space,” Robotics and Autonomous Systems , vol. 141, p. 103786, 2021
2021
Later among the works it cites.
A. Nouman, V. Patoglu, and E. Erdem, “Hybrid conditional planning for robotic applications,” The International Journal of Robotics Research , vol. 40, no. 2-3, pp. 594–623, 2021
2021
Later among the works it cites.
Y. Ding, X. Zhang, X. Zhan, and S. Zhang, “Learning to ground objects for robot task and motion planning,” in IEEE Robotics and Automation Letters , 2022
2022
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