Constraint propagation on interval bounds for dealing with geometric backtracking

Lagriffoul, Fabien; Dimitrov, Dimitar; Saffiotti, Alessandro; Karlsson, Lars

doi:10.1109/iros.2012.6385972

Cited by 61 publications

(56 citation statements)

References 6 publications

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“…Lagriffoul et al [25,26] interleave the search for a discrete action sequence and the geometric parameters and focus on limiting the amount of geometric backtracking. They generate a set of approximate linear constraints imposed by a plan skeleton under consideration, e.g., from grasp and placement choices, and use linear programming to compute a valid assignment or determine that one does not exist.…”

Section: Related Workmentioning

confidence: 99%

Sample-Based Methods for Factored Task and Motion Planning

Garrett¹,

Lozano-Pérez²,

Kaelbling³

2017

Robotics: Science and Systems XIII

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Abstract-There has been a great deal of progress in developing probabilistically complete methods that move beyond motion planning to multi-modal problems including various forms of task planning. This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and action spaces. The formulation characterizes conditions on the submanifolds in which solutions lie, leading to a characterization of robust feasibility that incorporates dimensionality-reducing constraints. It then connects those conditions to corresponding conditional samplers that are provided as part of a domain specification. We present domain-independent sample-based planning algorithms and show that they are both probabilistically complete and computationally efficient on a set of challenging benchmark problems.

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Section: Related Workmentioning

confidence: 99%

Sample-Based Methods for Factored Task and Motion Planning

Garrett¹,

Lozano-Pérez²,

Kaelbling³

2017

Robotics: Science and Systems XIII

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show abstract

“…Lagriffoul et al [12] also integrate the symbolic and geometric search. They generate a set of approximate linear constraints imposed by the program under consideration, e.g., from grasp and placement choices, and use linear programming to compute a valid assignment or determine one does not exist.…”

Section: Related Workmentioning

confidence: 99%

FFRob: An Efficient Heuristic for Task and Motion Planning

Garrett

Lozano-Pérez

Kaelbling

2015

Springer Tracts in Advanced Robotics

108

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Abstract. Manipulation problems involving many objects present substantial challenges for motion planning algorithms due to the high dimensionality and multi-modality of the search space. Symbolic task planners can efficiently construct plans involving many entities but cannot incorporate the constraints from geometry and kinematics. In this paper, we show how to extend the heuristic ideas from one of the most successful symbolic planners in recent years, the FastForward (FF) planner, to motion planning, and to compute it efficiently. We use a multi-query roadmap structure that can be conditionalized to model different placements of movable objects. The resulting tightly integrated planner is simple and performs efficiently in a collection of tasks involving manipulation of many objects.

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“…There has been increasing interest in the TAMP problem, with many suggested solutions for addressing the difficult multi-level search problem it entails [8], [9], [10], [11], [12], [13], [14]. Most methods ultimately involve a search at the task level in an abstract space, in which determining whether an operation is legal depends on the solution of a high-dimensional geometric motion-planning problem.…”

Section: Introductionmentioning

confidence: 99%

A constraint-based method for solving sequential manipulation planning problems

Lozano-Pérez

Kaelbling

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

142

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Abstract-In this paper, we describe 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. Partial plans (skeletons) in this search thus pose a geometric constraintsatisfaction problem (CSP), involving sequences of placements and paths for the robot, and grasps and locations of objects. We propose a formulation for these problems in a discretized configuration space for the robot. The resulting problems can be solved using existing methods for discrete CSP.

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Constraint propagation on interval bounds for dealing with geometric backtracking

Cited by 61 publications

References 6 publications

Sample-Based Methods for Factored Task and Motion Planning

Sample-Based Methods for Factored Task and Motion Planning

FFRob: An Efficient Heuristic for Task and Motion Planning

A constraint-based method for solving sequential manipulation planning problems

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