Integrated Task and Motion Planning

@article{Garrett2020IntegratedTA,
  title={Integrated Task and Motion Planning},
  author={Caelan R. Garrett and Rohan Chitnis and Rachel Holladay and Beomjoon Kim and Tom Silver and Leslie Pack Kaelbling and Tomas Lozano-Perez},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.01083}
}
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 paper, we define a class of TAMP… Expand

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References

SHOWING 1-10 OF 130 REFERENCES
Two manipulation planning algorithms
TLDR
This paper addresses the motion planning problem for a robot in presence of movable objects with an overview of a general approach which consists in building a manipulation graph whose connected components characterize the existence of solutions. Expand
A Hybrid Approach to Intricate Motion, Manipulation and Task Planning
TLDR
This work proposes a representation and a planning algorithm able to deal with problems integrating task planning as well as motion and manipulation planning knowledge involving several robots and objects and describes the main features of an implemented planner. Expand
A constraint-based method for solving sequential manipulation planning problems
TLDR
A strategy for integrated task and motion planning based on performing a symbolic search for a sequence of high-level operations, such as pick, move and place, while postponing geometric decisions is described. Expand
A Unified Sampling-Based Approach to Integrated Task and Motion Planning
We present a novel method for performing integrated task and motion planning (TMP) by adapting any off-the-shelf sampling-based motion planning algorithm to simultaneously solve for a symbolicallyExpand
Combined task and motion planning through an extensible planner-independent interface layer
TLDR
This work proposes a new approach that uses off-the-shelf task planners and motion planners and makes no assumptions about their implementation and uses a novel representational abstraction that requires only that failures in computing a motion plan for a high-level action be identifiable and expressible in the form of logical predicates at the task level. Expand
Combined Task and Motion Planning for Mobile Manipulation
TLDR
A hierarchical planning system that finds high-quality kinematic solutions to task-level problems and takes advantage of subtask-specific irrelevance information, reusing optimal solutions to state-abstracted sub-problems across the search space. Expand
Integrated task and motion planning in belief space
TLDR
It is shown that a relatively small set of symbolic operators can give rise to task-oriented perception in support of the manipulation goals and form a vocabulary of logical expressions that describe sets of belief states, which are goals and subgoals in the planning process. Expand
Task planning using physics-based heuristics on manipulation actions
TLDR
A task and motion planning framework is proposed based on a modified version of the Fast-Forward task planner that is guided by physics-based knowledge that results in an efficient search of the state space and in the obtention of low-cost physically-feasible plans. Expand
The Task-Motion Kit: An Open Source, General-Purpose Task and Motion-Planning Framework
TLDR
Robots require novel reasoning systems to achieve complex objectives in new environments and reasoning about and computing continuous motions is in the realm of motion planning, which is referred to as task planning. Expand
Incremental Task and Motion Planning: A Constraint-Based Approach
TLDR
The Iteratively Deepened Task and Motion Planning method is probabilistically-complete and offers improved performance and generality compared to a similar, state-of-theart, probabilistic-complete planner. Expand
...
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