A compact representation of human single-object grasping

Puhlmann, Steffen; Heinemann, Fabian; Brock, Oliver; Maertens, Marianne

doi:10.1109/iros.2016.7759308

Cited by 28 publications

(23 citation statements)

References 13 publications

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“…Such methods are limited by the resolution of tactile sensors. Puhlmann et al [39] capture handtable contact during grasping with a touchscreen. Rogez et al [42] manually configure a hand model to match grasps from a taxonomy, and use connected component analysis on hand vertices intersecting with an object model to estimate contact regions on the hand.…”

Section: Datasets Of Human Graspsmentioning

confidence: 99%

ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging

Brahmbhatt

Ham

Kemp

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

166

102

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Grasping and manipulating objects is an important human skill. Since hand-object contact is fundamental to grasping, capturing it can lead to important insights. However, observing contact through external sensors is challenging because of occlusion and the complexity of the human hand. We present ContactDB, a novel dataset of contact maps for household objects that captures the rich handobject contact that occurs during grasping, enabled by use of a thermal camera. Participants in our study grasped 3D printed objects with a post-grasp functional intent. Con-tactDB includes 3750 3D meshes of 50 household objects textured with contact maps and 375K frames of synchronized RGB-D+thermal images. To the best of our knowledge, this is the first large-scale dataset that records detailed contact maps for human grasps. Analysis of this data shows the influence of functional intent and object size on grasping, the tendency to touch/avoid 'active areas', and the high frequency of palm and proximal finger contact. Finally, we train state-of-the-art image translation and 3D convolution algorithms to predict diverse contact patterns from object shape. Data, code and models are available at https://contactdb.cc.gatech.edu.

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Section: Datasets Of Human Graspsmentioning

confidence: 99%

ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging

Brahmbhatt

Ham

Kemp

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

166

102

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“…Figure 1 shows some examples reproducing experimentally observed behaviors, where a subject exploits a flat surface. In Puhlmann et al ( 2016 ), a taxonomy of ECE primitives is proposed. In Eppner et al ( 2015 ), the authors also show that impairing the use of the environment during grasping and manipulation sensibly decreases subjects’ performance, suggesting the central role of these strategies in humans.…”

Section: Introductionmentioning

confidence: 99%

Postural Hand Synergies during Environmental Constraint Exploitation

et al. 2017

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Humans are able to intuitively exploit the shape of an object and environmental constraints to achieve stable grasps and perform dexterous manipulations. In doing that, a vast range of kinematic strategies can be observed. However, in this work we formulate the hypothesis that such ability can be described in terms of a synergistic behavior in the generation of hand postures, i.e., using a reduced set of commonly used kinematic patterns. This is in analogy with previous studies showing the presence of such behavior in different tasks, such as grasping. We investigated this hypothesis in experiments performed by six subjects, who were asked to grasp objects from a flat surface. We quantitatively characterized hand posture behavior from a kinematic perspective, i.e., the hand joint angles, in both pre-shaping and during the interaction with the environment. To determine the role of tactile feedback, we repeated the same experiments but with subjects wearing a rigid shell on the fingertips to reduce cutaneous afferent inputs. Results show the persistence of at least two postural synergies in all the considered experimental conditions and phases. Tactile impairment does not alter significantly the first two synergies, and contact with the environment generates a change only for higher order Principal Components. A good match also arises between the first synergy found in our analysis and the first synergy of grasping as quantified by previous work. The present study is motivated by the interest of learning from the human example, extracting lessons that can be applied in robot design and control. Thus, we conclude with a discussion on implications for robotics of our findings.

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“…them and the typical layout of factory lines. During top-down grasps, humans tend to completely envelop the object with the fingers [6], [7]. This movement is commonly known as caging primitive, and it is the same action performed by many conventional robotic solutions.…”

Section: Softhandler Softhandler Softhandler Softhandler Softhandler mentioning

confidence: 99%

“…This movement is commonly known as caging primitive, and it is the same action performed by many conventional robotic solutions. In the top-down approach, humans tend to also have many interactions with the environment, such as with the planar surface on which the object is placed, to realize better grasps (highlighted in [6] and [7]).…”

Section: Softhandler Softhandler Softhandler Softhandler Softhandler mentioning

confidence: 99%

“…Although the SoftGripper shares some of the basic components of the Pisa/IIT SoftHand (e.g., compliance through underactuation), it is completely different in most respects, including the kinematics and the number and length of the fingers. Whereas the SoftHand was designed to reproduce the first synergy observed in human grasps and has an architecture particularly suited for versatile grasping, the Soft-Gripper was inspired by the caging primitive [6], [7], that is, a grasping approach in which the object is fully enveloped by the fingers. Furthermore, the different kinematics and longer fingers of the SoftGripper allow it to grasp larger objects.…”

Section: Softhandler Designmentioning

confidence: 99%

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