Sample-Based Methods for Factored Task and Motion Planning

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

doi:10.15607/rss.2017.xiii.039

Cited by 39 publications

(37 citation statements)

References 31 publications

(33 reference statements)

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“…A common limitation of the TAMP methods above is that planning is slow, taking on the order of tens of seconds to minutes. To this end, Garrett et al [18] reduce the sampling space of motion planning by conditionally sampling on factoring patterns in the action skeletons. Wells et al [19] use a Support Vector Machine (SVM) to estimate the feasibility of actions to guide the symbolic search, only calling the motion planner on action skeletons classified by the SVM as feasible.…”

Section: Related Workmentioning

confidence: 99%

Object-Centric Task and Motion Planning in Dynamic Environments

Migimatsu

Bohg

2020

IEEE Robot. Autom. Lett.

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We address the problem of applying Task and Motion Planning (TAMP) in real world environments. TAMP combines symbolic and geometric reasoning to produce sequential manipulation plans, typically specified as joint-space trajectories, which are valid only as long as the environment is static and perception and control are highly accurate. In case of any changes in the environment, slow re-planning is required. We propose a TAMP algorithm that optimizes over Cartesian frames defined relative to target objects. The resulting plan then remains valid even if the objects are moving and can be executed by reactive controllers that adapt to these changes in real time. We apply our TAMP framework to a torque-controlled robot in a pick and place setting and demonstrate its ability to adapt to changing environments, inaccurate perception, and imprecise control, both in simulation and the real world.

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Section: Related Workmentioning

confidence: 99%

Object-Centric Task and Motion Planning in Dynamic Environments

Migimatsu

Bohg

2020

IEEE Robot. Autom. Lett.

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“…Robot task and motion (TAMP) problems are typically formulated as discrete-time planning problems in a hybrid discrete-continuous state transition system [16], [17], with discrete variables modeling which objects are being manipulated and other task-level aspects of the domain, and continuous variables modeling the robot configuration, object poses, and other continuous properties of the world.…”

Section: A Task and Motion Planningmentioning

confidence: 99%

“…We now define a TAMP problem, using some definitions from Garrett et al [16]. A predicate is a Boolean function.…”

Section: A Task and Motion Planningmentioning

confidence: 99%

Learning Quickly to Plan Quickly Using Modular Meta-Learning

Chitnis¹,

Kaelbling²,

Lozano-Pérez³

2019

2019 International Conference on Robotics and Automation (ICRA)

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Multi-object manipulation problems in continuous state and action spaces can be solved by planners that search over sampled values for the continuous parameters of operators. The efficiency of these planners depends critically on the effectiveness of the samplers used, but effective sampling in turn depends on details of the robot, environment, and task. Our strategy is to learn functions called specializers that generate values for continuous operator parameters, given a state description and values for the discrete parameters. Rather than trying to learn a single specializer for each operator from large amounts of data on a single task, we take a modular meta-learning approach. We train on multiple tasks and learn a variety of specializers that, on a new task, can be quickly adapted using relatively little data -thus, our system learns quickly to plan quickly using these specializers. We validate our approach experimentally in simulated 3D pick-and-place tasks with continuous state and action spaces. Visit http://tinyurl.com/chitnis-icra-19 for a supplementary video.

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“…To alleviate the complexity, a divide and conquer strategy has been widely used [2], [18], [19]. By partitioning the entire planning problem into a set of sub-problems (e.g., goal configuration generation and path planning separately for base body and manipulator), it can efficiently subdivide the search space into a set of sub-problem, while achieving a reasonable quality of the robot trajectory.…”

Section: B Mobile Manipulation Planningmentioning

confidence: 99%

Harmonious Sampling for Mobile Manipulation Planning

Kang¹,

Kim²,

Yoon

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Mobile manipulation planning commonly adopts a decoupled approach that performs planning separately on the base and the manipulator. While this approach is fast, it can generate sub-optimal paths. Another direction is a coupled approach jointly adjusting the base and manipulator in a high-dimensional configuration space. This coupled approach addresses sub-optimality and incompleteness of the decoupled approach, but has not been widely used due to its excessive computational overhead. Given this trade-off space, we present a simple, yet effective mobile manipulation sampling method, harmonious sampling, to perform the coupled approach mainly in difficult regions, where we need to simultaneously maneuver the base and the manipulator. Our method identifies such difficult regions through a low-dimensional base space by utilizing a reachability map given the target end-effector pose and narrow passage detected by generalized Voronoi diagram. For the rest of simple regions, we perform sampling mainly on the base configurations with a predefined joint configuration, accelerating the planning process. We compare our method with the decoupled and coupled approaches in six different problems with varying difficulty. Our method shows meaningful improvements experimentally in terms of time to find an initial solution (up to 5.6 times faster) and final solution cost (up to 17% lower) over the decoupled approach, especially in difficult scenes with narrow space. We also demonstrate these benefits with a real, mobile Hubo robot.

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Sample-Based Methods for Factored Task and Motion Planning

Cited by 39 publications

References 31 publications

Object-Centric Task and Motion Planning in Dynamic Environments

Object-Centric Task and Motion Planning in Dynamic Environments

Learning Quickly to Plan Quickly Using Modular Meta-Learning

Harmonious Sampling for Mobile Manipulation Planning

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