Incremental Task and Motion Planning: A Constraint-Based Approach

Dantam, Neil T.; Kingston, Zachary; Chaudhuri, Swarat; Kavraki, Lydia E.

doi:10.15607/rss.2016.xii.002

Cited by 147 publications

(122 citation statements)

References 57 publications

(100 reference statements)

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“…This problem can also be thought of as a special instance of hierarchical planning, with a discrete selection of constraint modality followed by geometric constrained planning. Footstep planning and other task and motion planning problems can all be thought of within this framework (36)(37)(38)(39)(40). Each of these planners employs domain-specific knowledge to solve the problem efficiently, but to the best of our knowledge, no general-purpose solutions have been proposed.…”

Section: Constraint Compositionmentioning

confidence: 99%

Sampling-Based Methods for Motion Planning with Constraints

Kingston

Moll

Kavraki

2018

Annu. Rev. Control Robot. Auton. Syst.

148

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Robots with many degrees of freedom (e.g., humanoid robots and mobile manipulators) have increasingly been employed to accomplish realistic tasks in domains such as disaster relief, spacecraft logistics, and home caretaking. Finding feasible motions for these robots autonomously is essential for their operation. Sampling-based motion planning algorithms are effective for these high-dimensional systems; however, incorporating task constraints (e.g., keeping a cup level or writing on a board) into the planning process introduces significant challenges. This survey describes the families of methods for sampling-based planning with constraints and places them on a spectrum delineated by their complexity. Constrained sampling-based methods are based on two core primitive operations: (a) sampling constraintsatisfying configurations and (b) generating constraint-satisfying continuous motion. Although this article presents the basics of sampling-based planning for contextual background, it focuses on the representation of constraints and sampling-based planners that incorporate constraints. 3.1

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Section: Constraint Compositionmentioning

confidence: 99%

Sampling-Based Methods for Motion Planning with Constraints

Kingston

Moll

Kavraki

2018

Annu. Rev. Control Robot. Auton. Syst.

148

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“…More recently, [FerrerMestres et al, 2017] showed that in some domains all geometric information could be represented compactly in planning languages more expressive than PDDL, avoiding the need to make geometric queries during the planning process. Other authors [Erdem et al, 2011;Srivastava et al, 2013;Dantam et al, 2016] used the task planner as a partial or approximate representation of the underlying geometric task, which could be improved during search. For instance, [Erdem et al, 2011] used a high-level task planner to find an optimal task plan, then used a motion planner to attempt to find a kinematically feasible primitive solution to that task plan.…”

Section: Related Workmentioning

confidence: 99%

Admissible Abstractions for Near-optimal Task and Motion Planning

Vega-Brown

Roy

2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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We define an admissibility condition for abstractions expressed using angelic semantics and show that these conditions allow us to accelerate planning while preserving the ability to find the optimal motion plan. We then derive admissible abstractions for two motion planning domains with continuous state. We extract upper and lower bounds on the cost of concrete motion plans using local metric and topological properties of the problem domain. These bounds guide the search for a plan while maintaining performance guarantees. We show that abstraction can dramatically reduce the complexity of search relative to a direct motion planner. Using our abstractions, we find near-optimal motion plans in planning problems involving 10 13 states without using a separate task planner.

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“…Dantam et al [6] formulate task and motion planning as a satisfiability modulo theories (SMT) problem. They use an incremental constraint solver to add motion constraints to the task-level logical formula when a candidate task plan is found.…”

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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Incremental Task and Motion Planning: A Constraint-Based Approach

Cited by 147 publications

References 57 publications

Sampling-Based Methods for Motion Planning with Constraints

Sampling-Based Methods for Motion Planning with Constraints

Admissible Abstractions for Near-optimal Task and Motion Planning

Sample-Based Methods for Factored Task and Motion Planning

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