MOPL: A multi-modal path planner for generic manipulation tasks

Jentzsch, Sören; Gaschler, Andre; Khatib, Oussama; Knoll, Alois

doi:10.1109/iros.2015.7354263

Cited by 11 publications

(10 citation statements)

References 14 publications

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“…As in our rearrangement approach, interactions between objects and end-effectors in [7] are modeled using an MDP near contact manifolds only, while sample-based planners are used in the other regions. DARRT is a sampling-based algorithm for general-purpose motion planning problems with diverse, non-prehensile manipulation actions [8], based on the RRT structure [9]. Our approach also uses RRT and non-prehensile manipulation actions, but it is tailored for efficient rearrangement.…”

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%

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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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Section: Related Workmentioning

confidence: 99%

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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“…Their focus was on the high level planning of manipulation sequence. Similarly, Jentzsch et al [18] used regrasp to solve multi-modal pick-and-place problems. Lee et al [19] also used non-prehensile grasp to plan sequential manipulation and reorient.…”

Section: Reorienting Objects Using Regrasp Planningmentioning

confidence: 99%

Regrasp planning using 10,000s of grasps

Wan

Harada

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper develops intelligent algorithms for robots to reorient objects. Given the initial and goal poses of an object, the proposed algorithms plan a sequence of robot poses and grasp configurations that reorient the object from its initial pose to the goal. While the topic has been studied extensively in previous work, this paper makes important improvements in grasp planning by using over-segmented meshes, in data storage by using relational database, and in regrasp planning by mixing real-world roadmaps. The improvements enable robots to do robust regrasp planning using 10,000s of grasps and their relationships in interactive time. The proposed algorithms are validated using various objects and robots.

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“…To make use of these non-grasp policies, Lee et al [23] proposed the concept of extended transit, i.e., the transition motion will not only include the transitions between prehensile grasps, but also those between non-prehensile manipulation strategies. A similar idea was also proposed by [24], which used grasping and pushing for transition motion, but only used grasping for transfer motion.…”

Section: B Sequential Robotic Manipulationmentioning

confidence: 99%

Analyzing the utility of a support pin in sequential robotic manipulation

Cao

Wan

Pan

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Pick-and-place regrasp is an important manipulation skill for a robot. It helps a robot accomplish tasks that cannot be achieved within a single grasp, due to constraints such as kinematics or collisions between the robot and the environment. Previous work on pick-and-place regrasp only leveraged flat surfaces for intermediate placements, and thus is limited in the capability to reorient an object.In this paper, we extend the reorientation capability of a pick-and-place regrasp by adding a vertical pin on the working surface and using it as the intermediate location for regrasping. In particular, our method automatically computes the stable placements of an object leaning against a vertical pin, finds several force-closure grasps, generates a graph of regrasp actions, and searches for the regrasp sequence. To compare the regrasping performance with and without using pins, we evaluate the success rate and the length of regrasp sequences while performing tasks on various models. Experiments on reorientation and assembly tasks validate the benefit of using support pins for regrasping.

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MOPL: A multi-modal path planner for generic manipulation tasks

Cited by 11 publications

References 14 publications

Object Rearrangement with Nested Nonprehensile Manipulation Actions

Object Rearrangement with Nested Nonprehensile Manipulation Actions

Regrasp planning using 10,000s of grasps

Analyzing the utility of a support pin in sequential robotic manipulation

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