Bimanual regrasping from unimanual machine learning

Balaguer, Benjamin; Carpin, Stefano

doi:10.1109/icra.2012.6225095

Cited by 11 publications

(7 citation statements)

References 18 publications

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“…Our approach solves what this algorithm assumes, determining what handoffs are needed. Some work has been done to use optimization techniques to find handoffs between two arms [1]. While all of these approaches address the problem of moving an object between hands, none of them solve the discrete problem of finding the sequence of arms required to move the object to its goal.…”

Section: Related Workmentioning

confidence: 99%

Planning Single-arm Manipulations with n-Arm Robots

Cohen

Phillips²,

Likhachev

2014

Robotics: Science and Systems X

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Abstract-Many robotic systems are comprised of two or more arms. Such systems range from dual-arm household manipulators to factory floors populated with a multitude of industrial robotic arms. While the use of multiple arms increases the productivity of the system and extends dramatically its workspace, it also introduces a number of challenges. One such challenge is planning the motion of the arm(s) required to relocate an object from one location to another. This problem is challenging because it requires reasoning over which arms and in which order should manipulate the object, finding a sequence of valid handoff locations between the consecutive arms and finally choosing the grasps that allow for successful handoffs. In this paper, we show how to exploit the characteristics of this problem in order to construct a planner that can solve it effectively. We analyze our approach experimentally on a number of simulated examples ranging from a 2-arm system operating at a table to a 3-arm system working at a bar and to a 4-arm system in a factory setting.

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Section: Related Workmentioning

confidence: 99%

Planning Single-arm Manipulations with n-Arm Robots

Cohen

Phillips²,

Likhachev

2014

Robotics: Science and Systems X

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“…In contrast to previous approaches, Balaguer and Carpin [13] address bimanual regrasping of known objects using a model which was previously trained by supervised learning for unimanual grasps. Later, an optimizer searches for an actual handover configuration which minimizes the execution time.…”

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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“…It fails to output a solution in some situations. Balaguer et al [17] studies the dual-arm regrasp problem by estimating the two grasping positions for the two hands in the object?s local coordinate system. It optimizes the regrasp position and object orientation by minimizing the time needed to move the two hands to the estimated positions.…”

Section: B Dual-arm Regraspmentioning

confidence: 99%

Developing and Comparing Single-Arm and Dual-Arm Regrasp

Wan

Harada

2016

IEEE Robot. Autom. Lett.

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Abstract-The goal of this paper is to develop efficient regrasp algorithms for single-arm and dual-arm regrasp and compares the performance of single-arm and dual-arm regrasp by running the two algorithms thousands of times. We focus on pick-and-place regrasp which reorients an object from one placement to another by using a sequence of pick-ups and placedowns. After analyzing the simulation results, we find dual-arm regrasp is not necessarily better than single-arm regrasp: Dualarm regrasp is flexible. When the two hands can grasp the object with good clearance, dual-arm regrasp is better and has higher successful rate than single-arm regrasp. However, dualarm regrasp suffers from geometric constraints caused by the two arms. When the grasps overlap, dual-arm regrasp is bad. Developers need to sample grasps with high density to reduce overlapping. This leads to exploded combinatorics in previous methods, but is possible with the algorithms presented in this paper. Following the results, practitioners may choose singlearm or dual-arm robots by considering the object shapes and grasps. Meanwhile, they can reduce overlapping and implement practical dual-arm regrasp by using the presented algorithms.

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Bimanual regrasping from unimanual machine learning

Cited by 11 publications

References 18 publications

Planning Single-arm Manipulations with n-Arm Robots

Planning Single-arm Manipulations with n-Arm Robots

Autonomous Bimanual Functional Regrasping of Novel Object Class Instances

Developing and Comparing Single-Arm and Dual-Arm Regrasp

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