A two-fingered robot gripper with large object reorientation range

Bircher, Walter G.; Dollar, Aaron M.; Rojas, Nicolás

doi:10.1109/icra.2017.7989394

Cited by 33 publications

(17 citation statements)

References 21 publications

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“…Hands with high intrinsic dexterity often mimic the structure of the human hand (66). Alternatively, the hand can be simpler, and the endeffector is designed specifically for a particular task (67,68). Extrinsic dexterity is the ability to compensate for the lack of degrees of freedom by using external support, such as friction, gravity, and contact surfaces (69).…”

Section: Manipulations That Remain Difficultmentioning

confidence: 99%

Trends and challenges in robot manipulation

Billard

Kragić

2019

Science

547

236

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Dexterous manipulation is one of the primary goals in robotics. Robots with this capability could sort and package objects, chop vegetables, and fold clothes. As robots come to work side by side with humans, they must also become human-aware. Over the past decade, research has made strides toward these goals. Progress has come from advances in visual and haptic perception and in mechanics in the form of soft actuators that offer a natural compliance. Most notably, immense progress in machine learning has been leveraged to encapsulate models of uncertainty and to support improvements in adaptive and robust control. Open questions remain in terms of how to enable robots to deal with the most unpredictable agent of all, the human.

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Section: Manipulations That Remain Difficultmentioning

confidence: 99%

Trends and challenges in robot manipulation

Billard

Kragić

2019

Science

547

236

View full text Add to dashboard Cite

show abstract

“…In [28] an anthropomorphic twofinger gripper design was based on kinematic observations of human hand motion during WIHM translation and reorientation tasks. Non-anthropomorphic gripper designs to optimize object re-orientation are also present in [29]. In [30] a simple 2 DOF manipulator was combined with an iterative learning approach to establish object re-orientation control schemes.…”

Section: A Within Hand Manipulationmentioning

confidence: 99%

Using a Variable-Friction Robot Hand to Determine Proprioceptive Features for Object Classification During Within-Hand-Manipulation

Spiers

Morgan

Srinivasan

et al. 2020

IEEE Trans. Haptics

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Interactions with an object during within-hand manipulation (WIHM) constitutes an assortment of gripping, sliding, and pivoting actions. In addition to manipulation benefits, the reorientation and motion of the objects within-thehand also provides a rich array of additional haptic information via the interactions to the sensory organs of the hand. In this work, we utilize Variable Friction (VF) robotic fingers to execute a rolling WIHM on a variety of objects, while recording 'proprioceptive' actuator data, which is then used for object classification (i.e. without tactile sensors). Rather than handpicking a select group of features for this task, our approach begins with 66 general features, which are computed from actuator position and load profiles for each object-rolling manipulation, based on gradient changes. An Extra Trees classifier performs object classification while also ranking each feature's importance. Using only the 6 most-important 'Key Features' from the general set, a classification accuracy of 86% was achieved for distinguishing the 6 geometric objects included in our data set. Comparatively, when all 66 features are used, the accuracy is 89.8%.

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“…However, these systems have shown significant limitations and challenges for inhand manipulation in unstructured environments due to overconstrained structures, uncertainties, and compound errors and failures in actuation and sensing. Improving the in-hand manipulation ability of robot grippers without increasing their design and control complexity has since become an active area of research in recent years [5], [6], [7], [8], [9]. The most common in-hand manipulations for robot grippers that have been studied are sliding and rotating operations.…”

Section: Introductionmentioning

confidence: 99%

An Origami-Inspired Variable Friction Surface for Increasing the Dexterity of Robotic Grippers

Clark

Shen

et al. 2020

IEEE Robot. Autom. Lett.

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While the grasping capability of robotic grippers has shown significant development, the ability to manipulate objects within the hand is still limited. One explanation for this limitation is the lack of controlled contact variation between the grasped object and the gripper. For instance, human hands have the ability to firmly grip object surfaces, as well as slide over object faces, an aspect that aids the enhanced manipulation of objects within the hand without losing contact. In this letter, we present a parametric, origami-inspired thin surface capable of transitioning between a high friction and a low friction state, suitable for implementation as an epidermis in robotic fingers. A numerical analysis of the proposed surface based on its design parameters, force analysis, and performance in in-hand manipulation tasks is presented. Through the development of a simple two-fingered two-degree-of-freedom gripper utilizing the proposed variablefriction surfaces with different parameters, we experimentally demonstrate the improved manipulation capabilities of the hand when compared to the same gripper without changeable friction. Results show that the pattern density and valley gap are the main parameters that effect the in-hand manipulation performance. The origami-inspired thin surface with a higher pattern density generated a smaller valley gap and smaller height change, producing a more stable improvement of the manipulation capabilities of the hand.

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A two-fingered robot gripper with large object reorientation range

Cited by 33 publications

References 21 publications

Trends and challenges in robot manipulation

Trends and challenges in robot manipulation

Using a Variable-Friction Robot Hand to Determine Proprioceptive Features for Object Classification During Within-Hand-Manipulation

An Origami-Inspired Variable Friction Surface for Increasing the Dexterity of Robotic Grippers

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