Sampling-based methods for factored task and motion planning

Garrett, Caelan Reed; Lozano-Pérez, Tomás; Kaelbling, Leslie Pack

doi:10.1177/0278364918802962

Cited by 74 publications

(58 citation statements)

References 59 publications

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“…PDDLStream algorithms are domain-independent , meaning that they are able to solve PDDLStream problems without any additional problem information. The simplest PDDLStream algorithm, the incremental algorithm (Garrett et al, 2017, 2018), iteratively alternates between a sampling and a searching phase. During its sampling phase, it passes all legal combinations of input values to each stream and attempts to sample new output values.…”

Section: B1 Pddlmentioning

confidence: 99%

“…More advanced algorithms can also be applied using the exact same PDDLStream problem description. For example, the focused algorithm (Garrett et al, 2017, 2018) first searches over plan skeletons , plans with free parameters, before attempting to sample values for the parameters. This allows focused to more intelligently identify which samplers are relevant for solving the task.…”

Section: B1 Pddlmentioning

confidence: 99%

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Learning compositional models of robot skills for task and motion planning

Wang

Garrett²,

Kaelbling³

et al. 2021

The International Journal of Robotics Research

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The objective of this work is to augment the basic abilities of a robot by learning to use sensorimotor primitives to solve complex long-horizon manipulation problems. This requires flexible generative planning that can combine primitive abilities in novel combinations and, thus, generalize across a wide variety of problems. In order to plan with primitive actions, we must have models of the actions: under what circumstances will executing this primitive successfully achieve some particular effect in the world? We use, and develop novel improvements to, state-of-the-art methods for active learning and sampling. We use Gaussian process methods for learning the constraints on skill effectiveness from small numbers of expensive-to-collect training examples. In addition, we develop efficient adaptive sampling methods for generating a comprehensive and diverse sequence of continuous candidate control parameter values (such as pouring waypoints for a cup) during planning. These values become end-effector goals for traditional motion planners that then solve for a full robot motion that performs the skill. By using learning and planning methods in conjunction, we take advantage of the strengths of each and plan for a wide variety of complex dynamic manipulation tasks. We demonstrate our approach in an integrated system, combining traditional robotics primitives with our newly learned models using an efficient robot task and motion planner. We evaluate our approach both in simulation and in the real world through measuring the quality of the selected primitive actions. Finally, we apply our integrated system to a variety of long-horizon simulated and real-world manipulation problems.

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Section: B1 Pddlmentioning

confidence: 99%

Section: B1 Pddlmentioning

confidence: 99%

Learning compositional models of robot skills for task and motion planning

Wang

Garrett²,

Kaelbling³

et al. 2021

The International Journal of Robotics Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, Şucan and Kavraki (2011) implemented multi-modal planning via acyclic task-motion multigraphs, and HPP implements multi-modal planning via constraint graphs (Mirabel et al, 2016). Multi-modal planning is also closely related to task and motion planning , which takes a more hierarchical approach to planning multi-modal paths (Dantam et al, 2018; Garrett et al, 2018; Srivastava et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

Exploring implicit spaces for constrained sampling-based planning

Kingston

Moll

Kavraki

2019

The International Journal of Robotics Research

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We present a review and reformulation of manifold constrained sampling-based motion planning within a unifying framework, IMACS (implicit manifold configuration space). IMACS enables a broad class of motion planners to plan in the presence of manifold constraints, decoupling the choice of motion planning algorithm and method for constraint adherence into orthogonal choices. We show that implicit configuration spaces defined by constraints can be presented to sampling-based planners by addressing two key fundamental primitives, sampling and local planning, and that IMACS preserves theoretical properties of probabilistic completeness and asymptotic optimality through these primitives. Within IMACS, we implement projection- and continuation-based methods for constraint adherence, and demonstrate the framework on a range of planners with both methods in simulated and realistic scenarios. Our results show that the choice of method for constraint adherence depends on many factors and that novel combinations of planners and methods of constraint adherence can be more effective than previous approaches. Our implementation of IMACS is open source within the Open Motion Planning Library and is easily extended for novel planners and constraint spaces.

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“…A key challenge is that often physical constraints such as collision, kinematic, and visibility constraints can restrict which high-level actions are feasible. Readers are referred to [17] for a more complete review of the work in this area.…”

Section: Task and Motion Planningmentioning

confidence: 99%

Automated sequence and motion planning for robotic spatial extrusion of 3D trusses

2018

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While robotic spatial extrusion has demonstrated a new and efficient means to fabricate 3D truss structures in architectural scale, a major challenge remains in automatically planning extrusion sequence and robotic motion for trusses with unconstrained topologies. This paper presents the first attempt in the field to rigorously formulate the extrusion sequence and motion planning (SAMP) problem, using a CSP encoding. Furthermore, this research proposes a new hierarchical planning framework to solve the extrusion SAMP problems that usually have a long planning horizon and 3D configuration complexity. By decoupling sequence and motion planning, the planning framework is able to efficiently solve the extrusion sequence, end-effector poses, joint configurations, and transition trajectories for spatial trusses with nonstandard topologies. This paper also presents the first detailed computation data to reveal the runtime bottleneck on solving SAMP problems, which provides insight and comparing baseline for future algorithmic development. Together with the algorithmic results, this paper also presents an open-source and modularized software implementation called Choreo that is machine-agnostic. To demonstrate the power of this algorithmic framework, three case studies, including real fabrication and simulation results, are presented.

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Sampling-based methods for factored task and motion planning

Cited by 74 publications

References 59 publications

Learning compositional models of robot skills for task and motion planning

Learning compositional models of robot skills for task and motion planning

Exploring implicit spaces for constrained sampling-based planning

Automated sequence and motion planning for robotic spatial extrusion of 3D trusses

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