Accelerating Motion Planning for Learned Mobile Manipulation Tasks Using Task-Guided Gibbs Sampling

2019 ◽  
pp. 251-267
Author(s):  
Chris Bowen ◽  
Ron Alterovitz
Keyword(s):  
Motion Planning ◽  
Gibbs Sampling ◽  
Mobile Manipulation ◽  
Accelerating Motion

Task Assignment and Motion Planning for Bi-Manual Mobile Manipulation

2019 ◽  
Author(s):  
Shantanu Thakar ◽  
Ariyan Kabir ◽  
Prahar M. Bhatt ◽  
Rishi K. Malhan ◽  
Pradeep Rajendran ◽  
...  
Keyword(s):  
Motion Planning ◽  
Task Assignment ◽  
Mobile Manipulation

scholarly journals FFRob: Leveraging symbolic planning for efficient task and motion planning

2017 ◽  
Vol 37 (1) ◽  
pp. 104-136 ◽  
Author(s):  
Caelan Reed Garrett ◽  
Tomás Lozano-Pérez ◽  
Leslie Pack Kaelbling
Keyword(s):  
Motion Planning ◽  
Heuristic Search ◽  
General Purpose ◽  
Configuration Spaces ◽  
Mobile Manipulation ◽  
Extended Action ◽  
Geometric Search ◽  
Problem Instances ◽  
Hybrid Configuration ◽  
Planning Problems

Mobile manipulation problems involving many objects are challenging to solve due to the high dimensionality and multi-modality of their hybrid configuration spaces. Planners that perform a purely geometric search are prohibitively slow for solving these problems because they are unable to factor the configuration space. Symbolic task planners can efficiently construct plans involving many variables but cannot represent the geometric and kinematic constraints required in manipulation. We present the FFRob algorithm for solving task and motion planning problems. First, we introduce extended action specification (EAS) as a general purpose planning representation that supports arbitrary predicates as conditions. We adapt existing heuristic search ideas for solving strips planning problems, particularly delete-relaxations, to solve EAS problem instances. We then apply the EAS representation and planners to manipulation problems resulting in FFRob. FFRob iteratively discretizes task and motion planning problems using batch sampling of manipulation primitives and a multi-query roadmap structure that can be conditionalized to evaluate reachability under different placements of movable objects. This structure enables the EAS planner to efficiently compute heuristics that incorporate geometric and kinematic planning constraints to give a tight estimate of the distance to the goal. Additionally, we show FFRob is probabilistically complete and has a finite expected runtime. Finally, we empirically demonstrate FFRob’s effectiveness on complex and diverse task and motion planning tasks including rearrangement planning and navigation among movable objects.

Extending and evaluating the posture-based model of motion planning

2006 ◽  
Author(s):  
Jonathan Vaughan ◽  
Steven Jax ◽  
David A. Rosenbaum
Keyword(s):  
Motion Planning

Motion Planning in ROS

2012 ◽  
Author(s):  
Ioan Sucan ◽  
Sachin Chitta
Keyword(s):  
Motion Planning

Gibbs Sampling for Marginal Posterior Expectations

1991 ◽  
Author(s):  
Alan E. Gelfand ◽  
Adrian F. Smith
Keyword(s):  
Gibbs Sampling

New Approaches in Randomized Preprocessing for Motion Planning,

1995 ◽  
Author(s):  
Sumanta Guha ◽  
Rama D. Puvvada ◽  
Deepti Suri ◽  
Ichiro Suzuki
Keyword(s):  
Motion Planning ◽  
New Approaches
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