Sampling-Based Motion and Symbolic Action Planning with geometric and differential constraints

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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“…The process can be viewed as using the task planner to guide the motion planning search. The work of Plaku and Hager [8] is similar in approach.…”

Section: Related Workmentioning

confidence: 97%

FFRob: An Efficient Heuristic for Task and Motion Planning

Garrett

Lozano-Pérez

Kaelbling

2015

Springer Tracts in Advanced Robotics

108

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“…In recent years, there have been a number of approaches to integrating discrete task planning and continuous motion planning [5,32,21,12,13,31,7,37], aimed at increasing the capabilities of autonomous robots.…”

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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“…Significant attention is paid to managing the search process, because the sample-based geometric planning methods never fail conclusively, making it critical to manage the allocation of time to different motion-planning problems well. Plaku and Hager [2010] extend this approach to handle robots with differential constraints and provide a utility-driven search strategy.…”

Section: Integrating Logical and Geometric Planningmentioning

confidence: 99%

Integrated Robot Task and Motion Planning in the Now

Kaelbling¹,

Lozano-Pérez

2012

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. This paper provides an approach to integrating geometric motion planning with logical task planning for long-horizon tasks in domains with many objects. We propose a tight integration between the logical and geometric aspects of planning. We use a logical representation which includes entities that refer to poses, grasps, paths and regions, without the need for a priori discretization. Given this representation and some simple mechanisms for geometric inference,we characterize the pre-conditions and effects of robot actions in terms of these logical entities. We then reason about the interaction of the geometric and non-geometric aspects of our domains using the general-purpose mechanism of goal regression (also known as pre-image backchaining). We propose an aggressive mechanism for temporal hierarchical decomposition, which postpones the pre-conditions of actions to create an abstraction hierarchy that both limits the lengths of plans that need to be generated and limits the set of objects relevant to each plan. We describe an implementation of this planning method and demonstrate it in a simulated kitchen environment in which it solves problems that require approximately 100 individual pick or place operations for moving multiple objects in a complex domain. This paper provides an approach to integrating geometric motion planning with logical task planning for long-horizon tasks in domains with many objects. We propose a tight integration between the logical and geometric aspects of planning. We use a logical representation which includes entities that refer to poses, grasps, paths and regions, without the need for a priori discretization. Given this representation and some simple mechanisms for geometric inference, we characterize the pre-conditions and effects of robot actions in terms of these logical entities.We then reason about the interaction of the geometric and non-geometric aspects of our domains using the general-purpose mechanism of goal regression (also known as pre-image backchaining). We propose an aggressive mechanism for temporal hierarchical decomposition, which postpones the pre-conditions of actions to create an abstraction hierarchy that both limits the lengths of plans that need t...

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Sampling-Based Motion and Symbolic Action Planning with geometric and differential constraints

Cited by 122 publications

References 19 publications

FFRob: An Efficient Heuristic for Task and Motion Planning

FFRob: An Efficient Heuristic for Task and Motion Planning

Sample-Based Methods for Factored Task and Motion Planning

Integrated Robot Task and Motion Planning in the Now

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