The DOI matches its Crossref record; 105 of the 105 references it cites were found in a registry or an index.
Integrated Task and Motion Planning
Open accessAbstract
The problem of planning for a robot that operates in environments containing a large number of objects, taking actions to move itself through the world as well as to change the state of the objects, is known as task and motion planning (TAMP). TAMP problems contain elements of discrete task planning, discrete–continuous mathematical programming, and continuous motion planning and thus cannot be effectively addressed by any of these fields directly. In this article, we define a class of TAMP problems and survey algorithms for solving them, characterizing the solution methods in terms of their strategies for solving the continuous-space subproblems and their techniques for integrating the discrete and continuous components of the search.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Kavraki, L. E., Svestka, P., Latombe, J.-C., & Overmars, M. H. (1996) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.508439 · Cited by 6,343
LaValle, S. M. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546877 · Cited by 5,211
STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving
Fikes, R. E., & Nilsson, N. J. (1971) · Artificial Intelligence · doi:10.1016/0004-3702(71)90010-5 · Cited by 4,882
LaValle, S. M. (2006) · Cited by 4,177
Randomized Kinodynamic Planning
LaValle, S. M., & Kuffner, J. J. (2001) · The International Journal of Robotics Research · doi:10.1177/02783640122067453 · Cited by 3,254
A Survey of Monte Carlo Tree Search Methods
Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., & Colton, S. (2012) · IEEE Transactions on Computational Intelligence and AI in Games · doi:10.1109/tciaig.2012.2186810 · Cited by 2,970
An algorithm for planning collision-free paths among polyhedral obstacles
Lozano-Pérez, T., & Wesley, M. A. (1979) · Communications of the ACM · doi:10.1145/359156.359164 · Cited by 2,304
Self-citationThe algorithmic analysis of hybrid systems
Alur, R., Courcoubetis, C., Halbwachs, N., Henzinger, T. A., Ho, P.-H., Nicollin, X., Olivero, A., Sifakis, J., & Yovine, S. (1995) · Theoretical Computer Science · doi:10.1016/0304-3975(94)00202-t · Cited by 1,876
The FF Planning System: Fast Plan Generation Through Heuristic Search
Hoffmann, J., & Nebel, B. (2001) · Journal of Artificial Intelligence Research · doi:10.1613/jair.855 · Cited by 1,838
Cited by
Foundation models in robotics: Applications, challenges, and the future
Firoozi, R., Tucker, J., Tian, S., Majumdar, A., Sun, J., Liu, W., Zhu, Y., Song, S., Kapoor, A., Hausman, K., et al. (2025) · The International Journal of Robotics Research · doi:10.1177/02783649241281508 · Cited by 238
Habitat 2.0: Training home assistants to rearrange their habitat
Szot, A., Clegg, A., Undersander, E., Wijmans, E., Zhao, Y., Turner, J., Maestre, N., Mukadam, M., Chaplot, D., Maksymets, O., et al. (2021) · arXiv · doi:10.48550/arxiv.2106.14405 · Cited by 143
Large language models for chemistry robotics
Yoshikawa, N., Skreta, M., Darvish, K., Arellano-Rubach, S., Ji, Z., Bjørn Kristensen, L., Li, A. Z., Zhao, Y., Xu, H., Kuramshin, A., et al. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10136-2 · Cited by 112
Rearrangement: A Challenge for Embodied AI
Batra, D., Chang, A. X., Chernova, S., Davison, A. J., Deng, J., Koltun, V., Levine, S., Malik, J., Mordatch, I., Mottaghi, R., et al. (2020) · arXiv · doi:10.48550/arxiv.2011.01975 · Cited by 109
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks
Nasiriany, S., Liu, H., & Zhu, Y. (2022) · 2022 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra46639.2022.9812140 · Cited by 91
Metadata
- Authors
- Garrett, C. R., Chitnis, R., Holladay, R., Kim, B., Silver, T., Kaelbling, L. P., & Lozano-Pérez, T.
- Venue
- Annual Review of Control, Robotics, and Autonomous Systems
- Publisher
- Annual Reviews
- Published
- 2021-05-03
- Volume
- 4
- Issue
- 1
- Pages
- 265-293
- ISSN
- 2573-5144 (ISSN-L)
- OpenAlex
- W3090293438
- Cited by
- 409
- References
- 105
- FWCI
- 9.33
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Integrated Task and Motion Planning. https://app.cochat.ai/papers/10.1146/annurev-control-091420-084139
- The paper
- Garrett, C. R., Chitnis, R., Holladay, R., Kim, B., Silver, T., Kaelbling, L. P., & Lozano-Pérez, T. (2021). Integrated Task and Motion Planning. Annual Review of Control, Robotics, and Autonomous Systems. https://doi.org/10.1146/annurev-control-091420-084139
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
