Developing and Comparing Single-Arm and Dual-Arm Regrasp

Wan, Weiwei; Harada, Kensuke

doi:10.1109/lra.2016.2517147

Cited by 75 publications

(52 citation statements)

References 24 publications

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“…6. It is basically the same as [29], except that we put the initial and goal states and their accessible grasps in the top and bottom layers, respectively. The top layer has only one circle which encodes the initial state of the object and its accessible grasps.…”

Section: Methodsmentioning

confidence: 99%

Integrated assembly and motion planning using regrasp graphs

Wan

Harada

2016

Robot. Biomim.

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This paper presents an integrated assembly and motion planning system to recursively find the assembly sequence and motions to assemble two objects with the help of a horizontal surface as the supporting fixture. The system is implemented in both assembly level and motion level. In the assembly level, the system checks all combinations of the assembly sequences and gets a set of candidates. Then, for each candidate assembly sequence, the system incrementally builds regrasp graphs and performs recursive search to find a pick-and-place motion in the motion level to manipulate the base object as well as to assemble the other object to the base. The system integrates the candidate assembly sequences computed in the assembly level incrementally and recursively with graph searching and motion planning in the motion level and plans the assembly sequences and motions integratedly for assembly tasks. Both simulation and real-world experiments are performed to demonstrate the efficacy of the integrated planning system.

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Section: Methodsmentioning

confidence: 99%

Integrated assembly and motion planning using regrasp graphs

Wan

Harada

2016

Robot. Biomim.

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“…The planner computes two motion sequences to realize tethered tool manipulation. The first motion sequence is an Object Manipulation Motion Sequence (OMMS), which is computed using our previously proposed single-arm manipulation planner [14] to manipulate the tool and place it in the desired pose. The second sequence, the Cable Manipulation Motion Sequence (CMMS), is used to modify the tool cable shape: The robot manipulates a cable slider to control the bending and the position of the cable.…”

Section: Manipulation Planning For Tethered Toolsmentioning

confidence: 99%

Tethered Tool Manipulation Planning With Cable Maneuvering

Sánchez

Wan

Harada

2020

IEEE Robot. Autom. Lett.

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In this paper, we present a planner for manipulating tethered tools using dual-armed robots. The planner generates robot motion sequences to maneuver a tool and its cable while avoiding robot-cable entanglements. Firstly, the planner generates an Object Manipulation Motion Sequence (OMMS) to handle the tool and place it in desired poses. Secondly, the planner examines the tool movement associated with the OMMS and computes candidate positions for a cable slider, to maneuver the tool cable and avoid collisions. Finally, the planner determines the optimal slider positions to avoid entanglements and generates a Cable Manipulation Motion Sequence (CMMS) to place the slider in these positions. The robot executes both the OMMS and CMMS to handle the tool and its cable to avoid entanglements and excess cable bending. Simulations and realworld experiments help validate the proposed method.

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“…Wan and Harada [10] compare single-arm regrasps with dual-arm regrasps. For single-arm regrasps, the authors assume a flat surface such as a table in front of the robot which is used to orient the object freely before the target grasp is possible, similar as in [3] and [5].…”

Section: Related Workmentioning

confidence: 99%

Autonomous Bimanual Functional Regrasping of Novel Object Class Instances

Pavlichenko

Rodríguez

Lenz

et al. 2019

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

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In human-made scenarios, robots need to be able to fully operate objects in their surroundings, i.e., objects are required to be functionally grasped rather than only picked. This imposes very strict constraints on the object pose such that a direct grasp can be performed. Inspired by the anthropomorphic nature of humanoid robots, we propose an approach that first grasps an object with one hand, obtaining full control over its pose, and performs the functional grasp with the second hand subsequently. Thus, we develop a fully autonomous pipeline for dual-arm functional regrasping of novel familiar objects, i.e., objects never seen before that belong to a known object category, e.g., spray bottles. This process involves semantic segmentation, object pose estimation, non-rigid mesh registration, grasp sampling, handover pose generation and in-hand pose refinement. The latter is used to compensate for the unpredictable object movement during the first grasp. The approach is applied to a human-like upper body. To the best knowledge of the authors, this is the first system that exhibits autonomous bimanual functional regrasping capabilities. We demonstrate that our system yields reliable success rates and can be applied on-line to real-world tasks using only one off-the-shelf RGB-D sensor.

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Developing and Comparing Single-Arm and Dual-Arm Regrasp

Cited by 75 publications

References 24 publications

Integrated assembly and motion planning using regrasp graphs

Integrated assembly and motion planning using regrasp graphs

Tethered Tool Manipulation Planning With Cable Maneuvering

Autonomous Bimanual Functional Regrasping of Novel Object Class Instances

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