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As robots play an increasingly important role in the industrial, the expectations about their applications for everyday living tasks are getting higher.
L. P. Kaelbling and T. Lozano-Perez, “Hierarchical task and motion planning in the now,” in 2011 IEEE International Conference on Robotics and Automation . IEEE, pp. 1470–1477
2011
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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 2011 IEEE International Conference on Robotics and Automation . IEEE, pp. 4575–4581
2011
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D. Berenson, P. Abbeel, and K. Goldberg, “A robot path planning framework that learns from experience,” in 2012 IEEE International Conference on Robotics and Automation . IEEE, pp. 3671–3678
2012
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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, pp. 639–646
2014
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T. Lozano-Perez and L. P. Kaelbling, “A constraint-based method for solving sequential manipulation planning problems,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, pp. 3684–3691
2014
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O. Arslan and P. Tsiotras, “Machine learning guided exploration for sampling-based motion planning algorithms,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, pp. 2646–2652
2015
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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, pp. 447–454
2016
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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, Sep. 2018
2018
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N. T. Dantam, S. Chaudhuri, and L. E. Kavraki, “The Task-Motion Kit: An Open Source, General-Purpose Task and Motion-Planning Framework,” IEEE Robotics & Automation Magazine , vol. 25, no. 3, pp. 61–70, Sep. 2018
B. Kim, Z. Wang, L. P. Kaelbling, and T. Lozano-Pérez, “Learning to guide task and motion planning using score-space representation,” The International Journal of Robotics Research , vol. 38, no. 7, pp. 793–812, Jun. 2019
2019
Later among the works it cites.
D. Driess, O. Oguz, J.-S. Ha, and M. Toussaint, “Deep Visual Heuristics: Learning Feasibility of Mixed-Integer Programs for Manipulation Planning,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . Paris, France: IEEE, May 2020, pp. 9563–9569
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, no. 1, pp. 265–293, May 2021
2021
Later among the works it cites.
T. Silver, R. Chitnis, J. Tenenbaum, L. P. Kaelbling, and T. Lozano-Pérez, “Learning symbolic operators for task and motion planning,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 3182–3189
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2018
Cited alongside, same era.
A. M. Wells, N. T. Dantam, A. Shrivastava, and L. E. Kavraki, “Learning Feasibility for Task and Motion Planning in Tabletop Environments,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 1255–1262, Apr. 2019
2019
Cited alongside, same era.
L. P. Kaelbling and T. Lozano-Pérez, “Integrated task and motion planning in belief space,” vol. 32, no. 9, pp. 1194–1227. [Online]. Available: http://journals.sagepub.com/doi/10.1177/0278364913484072
Cited in the paper.
R. E. Fikes and N. J. Nilsson, “Strips: A new approach to the application of theorem proving to problem solving,” vol. 2, no. 3, pp. 189–208
Cited in the paper.
Shakey the robot. https://www.sri.com/hoi/shakey-the-robot/
Cited in the paper.
M. Stilman and J. J. Kuffner, “Navigation among movable obstacles: real-time reasoning in complex environments,” p. 24
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S. Cambon, F. Gravot, and R. Alami, “A Robot Task Planner that Merges Symbolic and Geometric Reasoning,” p. 5
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F. Lagriffoul, D. Dimitrov, J. Bidot, A. Saffiotti, and L. Karlsson, “Efficiently combining task and motion planning using geometric constraints,” vol. 33, no. 14, pp. 1726–1747
Cited in the paper.
C. R. Garrett, T. Lozano-Pérez, and L. P. Kaelbling, “PDDLStream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning.”
Cited in the paper.
2021
Later among the works it cites.