Design and Control of Roller Grasper V2 for In-Hand Manipulation

Yuan, Shenli; Shao, Lin; Yako, Connor L.; Gruebele, Alex; Salisbury, J. Kenneth

doi:10.1109/iros45743.2020.9340953

Cited by 35 publications

(18 citation statements)

References 28 publications

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“…While this advantageously simplifies assumptions for control, it also limits the object's available workspace to be dependent on the kinematic topology of the hand. Without relying on external contact, e.g, [20], or task-specific hands with roller-based fingers, e.g [21], two general approaches can help alleviate such constraint: sliding manipulation and finger gaiting. Leveraging the former is very difficult, as detecting and controlling its nonlinear conditions requires various levels of advanced sensing [22], [23].…”

Section: Related Work 1) Modeling Manipulationmentioning

confidence: 99%

Complex In-Hand Manipulation via Compliance-Enabled Finger Gaiting and Multi-Modal Planning

Morgan,

Hang,

Wen

et al. 2022

Preprint

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Section: Related Work 1) Modeling Manipulationmentioning

confidence: 99%

Complex In-Hand Manipulation via Compliance-Enabled Finger Gaiting and Multi-Modal Planning

Morgan,

Hang,

Wen

et al. 2022

Preprint

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“…Most robot manipulation simulations often concentrate on solving specific manipulation problems, such as grasping [17,18] and in-hand manipulation [19][20][21]. The Recent MetaWorld [22] and RLBench [23] contain a variety of manipulation tasks for robot learning.…”

Section: A Simulation Environments For Robotic Manipulationmentioning

confidence: 99%

RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment

Yu¹,

Shen²,

Chen³

et al. 2021

Preprint

Self Cite

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Part assembly is a typical but challenging task in robotics, where robots assemble a set of individual parts into a complete shape. In this paper, we develop a robotic assembly simulation environment for furniture assembly. We formulate the part assembly task as a concrete reinforcement learning problem and propose a pipeline for robots to learn to assemble a diverse set of chairs. Experiments show that when testing with unseen chairs, our approach achieves a success rate of 74.5% under the object-centric setting and 50.0% under the full setting. We adopt an RRT-Connect [1] algorithm as the baseline, which only achieves a success rate of 18.8% after a significantly longer computation time. Supplemental materials and videos are available on our project webpage: https://sites.google.com/view/roboticassembly.

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“…These active surfaces provide tangential forces that allow in-hand manipulation of objects with a variety of shapes by a rotational movement. A three-finger gripper with compact rollers at the fingertips is presented in [15], [16], where the end-effector provides rotational in-hand manipulation using two-Degree-of-Freedom spheres that provide three contact points with an object. In [17], [18], robotic fingers have been proposed with variable friction surfaces.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Underactuated Finger With Active Rolling Surface

Gómez-de-Gabriel

Würdemann

2021

IEEE Robot. Autom. Lett.

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This paper presents the design, prototype and kinematic model of a new adaptive underactuated finger with an articulated skin/surface that is able to bend and, at the same time, provides active rolling motion along its central axis while keeping the finger configuration. The design is based on a planar chain of overlapping spherical phalanxes that are tendon-driven. The finger has an articulated surface made of an external chain of hollow universal joints that can rotate via its central axis on the surface of the internal structure. The outer surface provides a second active Degree of Freedom (DoF). The two actuators, driving the bending and/or rolling motion, can be used independently. A set of experiments have been included to validate and measure the performance of the prototype for the grasping and rolling actions. The proposed finger can be built with a different number of phalanxes and sizes. A number of these fingers can be arranged along a palm structure resulting in a multi-finger robotic grasper for applications that require adaptation and in-hand manipulation capabilities such as pHRI.

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Design and Control of Roller Grasper V2 for In-Hand Manipulation

Cited by 35 publications

References 28 publications

Complex In-Hand Manipulation via Compliance-Enabled Finger Gaiting and Multi-Modal Planning

Complex In-Hand Manipulation via Compliance-Enabled Finger Gaiting and Multi-Modal Planning

RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment

Adaptive Underactuated Finger With Active Rolling Surface

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