Integrated Task and Motion Planning

By Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay, Beomjoon Kim, Tom Silver, Leslie Pack Kaelbling, Tomas Lozano-Perez. 2020

718 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition, Robotics & Control

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Citations in the corpus

Habitat 2.0: Training Home Assistants to Rearrange their Habitat (2021, background): “Task and motion planning [55] and mobile manipulation have a long history in AI and robotics, whose full survey is beyond the scope of this document.”
Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation (2022, background): “In the sub-field of Task and Motion Planning (TAMP) [80, 81], Konidaris et al.”
Inventing Relational State and Action Abstractions for Effective and Efficient Bilevel Planning (2022, methodology): “To use the components of an abstraction — predicates Ψ , operators Ω , and samplers Σ — for efﬁcient planning, we build on bilevel planning techniques [9, 10].”
Discovering State and Action Abstractions for Generalized Task and Motion Planning (2021, methodology): “One typical approach to solving this type of problem is task and motion planning (TAMP) (Garrett et al. 2020).”
Large Language Models as Commonsense Knowledge for Large-Scale Task Planning (2023, background): “In the robotics community, many studies proposed that task planning should be integrated with physical-level motion planning, i.e., task and motion planning (TAMP) [11, 12, 9].”
ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation (2024, methodology): “For sequential manipulation tasks, task and motion planning (TAMP) [1, 8, 13] is a widely used framework, often formulated as constraint satisfaction problems [11, 75–80] with continuous geometric problems as subroutines.”
Foundation models in robotics: Applications, challenges, and the future (2023, neutral): “We have delved into some of these challenges and have discussed potential avenues for improvement.”
Rearrangement: A Challenge for Embodied AI (2020, methodology): “The combination of task planning and motion planning has also been explored, for example by Kaelbling and Lozano-P´erez [39, 29].”
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark Towards Physically Realistic Embodied AI (2021, positive): “Our work falls in the domain of task and motion planning (TAMP) [22, 23, 18, 42], recently reviewed in [17].”
Text2Motion: from natural language instructions to feasible plans (2023, methodology): “Task and Motion Planning (TAMP) refers to a problem setting in which a robot solves long-horizon tasks through symbolic and geometric reasoning [26, 27].”