Asymptotically Optimal Planning under Piecewise-Analytic Constraints

Vega-Brown, William; Roy, Nicholas

doi:10.1007/978-3-030-43089-4_34

Cited by 44 publications

(41 citation statements)

References 15 publications

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“…Approaches that are probabilistically complete include [20], which achieves asymptotic optimality in planar manipulation domains. The aSyMov planner [21], [22] uses an FF based task planner with lazily-expanded roadmaps; roadmaps allow plan re-use but present difficulty when configuration spaces change as objects interact-e.g., when stacking and pushing.…”

Section: Task and Motion Planningmentioning

confidence: 99%

Learning Feasibility for Task and Motion Planning in Tabletop Environments

Wells

Dantam

Shrivastava

et al. 2019

IEEE Robot. Autom. Lett.

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Task and motion planning (TMP) combines discrete search and continuous motion planning. Earlier work has shown that to efficiently find a task-motion plan, the discrete search can leverage information about the continuous geometry. However, incorporating continuous elements into discrete planners presents challenges. We improve the scalability of TMP algorithms in tabletop scenarios with a fixed robot by introducing geometric knowledge into a constraint-based task planner in a robust way. The key idea is to learn a classifier for feasible motions and to use this classifier as a heuristic to order the search for a task-motion plan. The learned heuristic guides the search towards feasible motions and thus reduces the total number of motion planning attempts. A critical property of our approach is allowing robust planning in diverse scenes. We train the classifier on minimal exemplar scenes and then use principled approximations to apply the classifier to complex scenarios in a way that minimizes the effect of errors. By combining learning with planning, our heuristic yields order-of-magnitude run time improvements in diverse tabletop scenarios. Even when classification errors are present, properly biasing our heuristic ensures we will have little computational penalty.

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Section: Task and Motion Planningmentioning

confidence: 99%

Learning Feasibility for Task and Motion Planning in Tabletop Environments

Wells

Dantam

Shrivastava

et al. 2019

IEEE Robot. Autom. Lett.

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“…A smaller number of other task and motion planners also achieve probabilistic completeness. Vega-Brown and Roy (2016) achieve even asymptotic optimality, demonstrating results for planar manipulation. In contrast, our framework focuses on handling high-DOF manipulation domains rather than producing optimal plans.…”

Section: Related Workmentioning

confidence: 76%

An incremental constraint-based framework for task and motion planning

Dantam

Kingston

Chaudhuri

et al. 2018

The International Journal of Robotics Research

108

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We present a new constraint-based framework for task and motion planning (TMP). Our approach is extensible, probabilistically complete, and offers improved performance and generality compared with a similar, state-of-the-art planner. The key idea is to leverage incremental constraint solving to efficiently incorporate geometric information at the task level. Using motion feasibility information to guide task planning improves scalability of the overall planner. Our key abstractions address the requirements of manipulation and object rearrangement. We validate our approach on a physical manipulator and evaluate scalability on scenarios with many objects and long plans, showing order-of-magnitude gains compared with the benchmark planner and improved scalability from additional geometric guidance. Finally, in addition to describing a new method for TMP and its implementation on a physical robot, we also put forward requirements and abstractions for the development of similar planners in the future.

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“…Hauser and Latombe (2010) addressed the multi-modal planning problem for a finite number of modes, which was extended for infinite modes in Hauser and Ng-Thow-Hing (2011). An asymptotically optimal multi-modal planning algorithm was proposed by Vega-Brown and Roy (2016). 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).…”

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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Asymptotically Optimal Planning under Piecewise-Analytic Constraints

Cited by 44 publications

References 15 publications

Learning Feasibility for Task and Motion Planning in Tabletop Environments

Learning Feasibility for Task and Motion Planning in Tabletop Environments

An incremental constraint-based framework for task and motion planning

Exploring implicit spaces for constrained sampling-based planning

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