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Probabilistic Approach to Physical Object Disentangling

Pajarinen, Joni ; Arenz, Oleg ; Peters, Jan ; Neumann, Gerhard (2022)
Probabilistic Approach to Physical Object Disentangling.
In: IEEE Robotics and Automation Letters, 5 (4)
doi: 10.26083/tuprints-00022927
Article, Secondary publication, Postprint

Abstract

Physically disentangling entangled objects from each other is a problem encountered in waste segregation or in any task that requires disassembly of structures. Often there are no object models, and especially with cluttered irregularly shaped objects, the robot cannot create a model of the scene due to occlusion. One of our key insights is that based on previous sensory input we are only interested in moving an object out of the disentanglement around obstacles. That is, we only need to know where the robot can successfully move in order to plan the disentangling. Due to the uncertainty we integrate information about blocked movements into a probability map. The map defines the probability of the robot successfully moving to a specific configuration. Using as cost the failure probability of a sequence of movements we can then plan and execute disentangling iteratively. Since our approach circumvents only previously encountered obstacles, new movements will yield information about unknown obstacles that block movement until the robot has learned to circumvent all obstacles and disentangling succeeds. In the experiments, we use a special probabilistic version of the Rapidly exploring Random Tree (RRT) algorithm for planning and demonstrate successful disentanglement of objects both in 2-D and 3-D simulation, and, on a KUKA LBR 7-DOF robot. Moreover, our approach outperforms baseline methods.

Item Type: Article
Erschienen: 2022
Creators: Pajarinen, Joni ; Arenz, Oleg ; Peters, Jan ; Neumann, Gerhard
Type of entry: Secondary publication
Title: Probabilistic Approach to Physical Object Disentangling
Language: English
Date: 2022
Place of Publication: Darmstadt
Publisher: IEEE
Journal or Publication Title: IEEE Robotics and Automation Letters
Volume of the journal: 5
Issue Number: 4
Collation: 9 ungezählte Seiten
DOI: 10.26083/tuprints-00022927
URL / URN: https://tuprints.ulb.tu-darmstadt.de/22927
Corresponding Links:
Origin: Secondary publication service
Abstract:

Physically disentangling entangled objects from each other is a problem encountered in waste segregation or in any task that requires disassembly of structures. Often there are no object models, and especially with cluttered irregularly shaped objects, the robot cannot create a model of the scene due to occlusion. One of our key insights is that based on previous sensory input we are only interested in moving an object out of the disentanglement around obstacles. That is, we only need to know where the robot can successfully move in order to plan the disentangling. Due to the uncertainty we integrate information about blocked movements into a probability map. The map defines the probability of the robot successfully moving to a specific configuration. Using as cost the failure probability of a sequence of movements we can then plan and execute disentangling iteratively. Since our approach circumvents only previously encountered obstacles, new movements will yield information about unknown obstacles that block movement until the robot has learned to circumvent all obstacles and disentangling succeeds. In the experiments, we use a special probabilistic version of the Rapidly exploring Random Tree (RRT) algorithm for planning and demonstrate successful disentanglement of objects both in 2-D and 3-D simulation, and, on a KUKA LBR 7-DOF robot. Moreover, our approach outperforms baseline methods.

Uncontrolled Keywords: Robot sensing systems, Collision avoidance, Path planning, Planning, Probabilistic logic, Task analysis, Autonomous systems, collision avoidance, intelligent robots, path planning, probabilistic computing, waste recovery
Status: Postprint
URN: urn:nbn:de:tuda-tuprints-229277
Additional Information:

Video attachment: https://t1p.de/r1a6d

The video illustrates the proposed approach for disentangling an object from other unknown objects.

Classification DDC: 000 Generalities, computers, information > 004 Computer science
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Intelligent Autonomous Systems
Date Deposited: 25 Nov 2022 12:48
Last Modified: 17 May 2023 12:48
PPN: 503603031
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