Rearrangement planning using object-centric and robot-centric action spaces

King, Jennifer E.; Cognetti, Marco; Srinivasa, Siddhartha S.

doi:10.1109/icra.2016.7487583

Cited by 61 publications

(35 citation statements)

References 18 publications

(19 reference statements)

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“…In contrast with FFRob's batch action sampling, HBF samples action primitives while simultaneously searching through the statespace. King et al (2016) investigated rearrangement planning with both object-centric motions, actions involving a particular object, and robot-centric motions, actions not involving any particular objects. Most of the presented literature involves only object-centric motions.…”

Section: Manipulation Planningmentioning

confidence: 99%

FFRob: Leveraging symbolic planning for efficient task and motion planning

Garrett

Lozano-Pérez

Kaelbling

2017

The International Journal of Robotics Research

128

106

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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 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.

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Section: Manipulation Planningmentioning

confidence: 99%

FFRob: Leveraging symbolic planning for efficient task and motion planning

Garrett

Lozano-Pérez

Kaelbling

2017

The International Journal of Robotics Research

128

106

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“…Object Rearrangement with Nonprehensile Actions: A framework that plans rearrangement of clutter using nonprehensile actions, such as pushing, was introduced by Dogar et al [21], but it did not consider nested actions. A version of RRT with Kinodynamics also used nonprehensile whole arm rearrangement planning [13], [22]. This approach is related to ours in the sense that whole arm manipulation can be seen as a special type of nested manipulation actions.…”

Section: Related Workmentioning

confidence: 99%

“…Path π main can be followed simultaneously by a train of other objects lined up behind the frontal object if they have a similar or smaller footprint. To this end, the algorithm proceeds into placing the remaining objects in the reverse order of list L (lines [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Note that L can also contain a single object as a special case.…”

Section: B Nested Pushingmentioning

confidence: 99%

Object Rearrangement with Nested Nonprehensile Manipulation Actions

Song

Boularias

2019

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

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This paper considers the problem of rearrangement planning, i.e finding a sequence of manipulation actions that displace multiple objects from an initial configuration to a given goal configuration. Rearrangement is a critical skill for robots so that they can effectively operate in confined spaces that contain clutter. Examples of tasks that require rearrangement include packing objects inside a bin, wherein objects need to lay according to a predefined pattern. In tight bins, collision-free grasps are often unavailable. Nonprehensile actions, such as pushing and sliding, are preferred because they can be performed using minimalistic end-effectors that can easily be inserted in the bin. Rearrangement with nonprehensile actions is a challenging problem as it requires reasoning about object interactions in a combinatorially large configuration space of multiple objects. This work revisits several existing rearrangement planning techniques and introduces a new one that exploits nested nonprehensile actions by pushing several similar objects simultaneously along the same path, which removes the need to rearrange each object individually. Experiments in simulation and using a real Kuka robotic arm show the ability of the proposed approach to solve difficult rearrangement tasks while reducing the length of the end-effector's trajectories.

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“…The propagation step is performed using a physics engine such as ODE. Within this framework, approaches are proposed for problems like motion planning among collisionable obstacles [19], rearrangement planning [20] [21], object placement [22], object sorting [23] or bin picking [24]. This paper will tackle the clutter grasping problem using a physics-based motion planner able to cope with uncertainty in the initial state of the system and in the poses of the objects as a result of robot-object or object-object interactions.…”

Section: Related Workmentioning

confidence: 99%

Randomized Physics-Based Motion Planning for Grasping in Cluttered and Uncertain Environments

Muhayyuddin

Moll

Kavraki

et al. 2018

IEEE Robot. Autom. Lett.

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Abstract-Planning motions to grasp an object in cluttered and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exist and objects obstructing the way are required to be carefully grasped and moved out. This paper takes a different approach and proposes to address this problem by using a randomized physics-based motion planner that permits robot-object and object-object interactions. The main idea is to avoid an explicit high-level reasoning of the task by providing the motion planner with a physics engine to evaluate possible complex multi-body dynamical interactions. The approach is able to solve the problem in complex scenarios, also considering uncertainty in the objects' pose and in the contact dynamics. The work enhances the state validity checker, the control sampler and the tree exploration strategy of a kinodynamic motion planner called KPIECE. The enhanced algorithm, called p-KPIECE, has been validated in simulation and with real experiments. The results have been compared with an ontological physics-based motion planner and with task and motion planning approaches, resulting in a significant improvement in terms of planning time, success rate and quality of the solution path.

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Rearrangement planning using object-centric and robot-centric action spaces

Cited by 61 publications

References 18 publications

FFRob: Leveraging symbolic planning for efficient task and motion planning

FFRob: Leveraging symbolic planning for efficient task and motion planning

Object Rearrangement with Nested Nonprehensile Manipulation Actions

Randomized Physics-Based Motion Planning for Grasping in Cluttered and Uncertain Environments

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