Object Rearrangement with Nested Nonprehensile Manipulation Actions

Song, Chang‐Kyu; Boularias, Abdeslam

doi:10.1109/iros40897.2019.8967548

Cited by 13 publications

(4 citation statements)

References 39 publications

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“…For manipulation setups where it is difficult or slow to perform grasping, non-prehensile actions are used for reconfiguring multiple objects at a time, which enables largescale object manipulation [14], [15]. Pushing actions are preferred in tasks, such as bin picking and sorting [16]- [18], as they can be performed with minimalistic endeffectors that can easily fit in a cluttered, constrained space. In harder problems, pushing and grasping actions are used interchangeably throughout the task [19], [20].…”

Section: Related Workmentioning

confidence: 99%

Persistent Homology for Effective Non-Prehensile Manipulation

Vieira¹,

Nakhimovich²,

Gao³

et al. 2022

Preprint

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This work explores the use of topological tools for achieving effective non-prehensile manipulation in cluttered, constrained workspaces. In particular, it proposes the use of persistent homology as a guiding principle in identifying the appropriate non-prehensile actions, such as pushing, to clean a cluttered space with a robotic arm so as to allow the retrieval of a target object. Persistent homology enables the automatic identification of connected components of blocking objects in the space without the need for manual input or tuning of parameters. The proposed algorithm uses this information to push groups of cylindrical objects together and aims to minimize the number of pushing actions needed to reach to the target. Simulated experiments in a physics engine using a model of the Baxter robot show that the proposed topologydriven solution is achieving significantly higher success rate in solving such constrained problems relatively to state-of-the-art alternatives from the literature. It manages to keep the number of pushing actions low, is computationally efficient and the resulting decisions and motion appear natural for effectively solving such tasks.

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Section: Related Workmentioning

confidence: 99%

Persistent Homology for Effective Non-Prehensile Manipulation

Vieira¹,

Nakhimovich²,

Gao³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In contrast, non-prehensile actions can be used in cluttered and constrained workspace with uncertainty to rearrange multiple objects using one action, thus allowing large-scale object manipulation [7], [8]. Sometimes they are preferred over picking actions [9]- [11], since pushing can be executed with basic and small end-effectors for cluttered, denser and constrained workspaces. More complicated problems may require both approaches [12], [13], where the tasks include interchangeably pushing and grasping actions.…”

Section: Related Workmentioning

confidence: 99%

Persistent Homology Guided Monte-Carlo Tree Search for Effective Non-Prehensile Manipulation

Vieira¹,

Gao²,

Nakhimovich³

et al. 2022

Preprint

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Performing object retrieval tasks in messy realworld workspaces involves the challenges of uncertainty and clutter. One option is to solve retrieval problems via a sequence of prehensile pick-n-place operations, which can be computationally expensive to compute in highly-cluttered scenarios and also inefficient to execute. The proposed framework selects the option of performing non-prehensile actions, such as pushing, to clean a cluttered workspace to allow a robotic arm to retrieve a target object. Non-prehensile actions, allow to interact simultaneously with multiple objects, which can speed up execution. At the same time, they can significantly increase uncertainty as it is not easy to accurately estimate the outcome of a pushing operation in clutter. The proposed framework integrates topological tools and Monte-Carlo tree search to achieve effective and robust pushing for object retrieval problems. In particular, it proposes using persistent homology to automatically identify manageable clustering of blocking objects in the workspace without the need for manually adjusting hyper-parameters. Furthermore, MCTS uses this information to explore feasible actions to push groups of objects together, aiming to minimize the number of pushing actions needed to clear the path to the target object. Real-world experiments using a Baxter robot, which involves some noise in actuation, show that the proposed framework achieves a higher success rate in solving retrieval tasks in dense clutter compared to state-of-the-art alternatives. Moreover, it produces high-quality solutions with a small number of pushing actions improving the overall execution time. More critically, it is robust enough that it allows to plan the sequence of actions offline and then execute them reliably online with Baxter.

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“…Recent progress in pushing include Zhou et al [14] and Yu et al [15], which use probabilistic models to infer object states. Song et al [16] proposed a nested approach to manipulate multiple objects together using pushing and learning. With the help of pushing, a robot can manipulate objects that cannot be directly grasped and lifted.…”

Section: Related Workmentioning

confidence: 99%

A Dual-arm Robot that Autonomously Lifts Up and Tumbles Heavy Plates Using Crane Pulley Blocks

Hayakawa¹,

Wan²,

Koyama³

et al. 2021

Preprint

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This paper develops a planner that plans the action sequences and motion for a dual-arm robot to lift up and flip heavy plates using crane pulley blocks. The problem is motivated by the low payload of modern collaborative robots. Instead of directly manipulating heavy plates that collaborative robots cannot afford, the paper develops a planner for collaborative robots to operate crane pulley blocks. The planner assumes a target plate is pre-attached to the crane hook. It optimizes dualarm action sequences and plans the robot's dual-arm motion that pulls the rope of the crane pulley blocks to lift up the plate. The crane pulley blocks reduce the payload that each robotic arm needs to bear. When the plate is lifted up to a satisfying pose, the planner plans a pushing motion for one of the robot arms to tumble over the plate while considering force and moment constraints. The article presents the technical details of the planner and several experiments and analysis carried out using a dual-arm robot made by two Universal Robots UR3 arms. The influence of various parameters and optimization goals are investigated and compared in depth. The results show that the proposed planner is flexible and efficient.

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Object Rearrangement with Nested Nonprehensile Manipulation Actions

Cited by 13 publications

References 39 publications

Persistent Homology for Effective Non-Prehensile Manipulation

Persistent Homology for Effective Non-Prehensile Manipulation

Persistent Homology Guided Monte-Carlo Tree Search for Effective Non-Prehensile Manipulation

A Dual-arm Robot that Autonomously Lifts Up and Tumbles Heavy Plates Using Crane Pulley Blocks

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