Vision-based grasp learning of an anthropomorphic hand-arm system in a synergy-based control framework | Science Robotics

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Fig. 1 Schematic overview of the proposed algorithm.
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Fig. 2 Example of point cloud processing.
(A) Original point cloud. (B) Processed point cloud. The main plane in the scene has been removed, and the segmentation of the remainder of the cloud has been executed.
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Fig. 3 Experiment on a tennis ball.
(A) Grasp success table. (B) Force-closure cost function. (C) Synergy coefficients. (D) Arm scores.
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Fig. 4 Experiment on a plastic strawberry.
(A) Grasp success table. (B) Force-closure cost function. (C) Synergy coefficients. (D) Arm scores.
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Fig. 5 Experiment on a plastic bottle.
(A) Grasp success table. (B) Force-closure cost function. (C) Synergy coefficients. (D) Arm scores.
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Fig. 6 Successful grasps of the tennis ball, strawberry, and plastic bottle at the end of the learning process.

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Table 1 Comparison of grasps obtained with and without force-closure cost function.
Force-closure cost value
Grasp Cost not used Cost used
Bipodal 1.9 × 10⁷ 8.5 × 10⁵
Tripodal 2.5 × 10⁷ 3.1 × 10⁶
Sphere five finger 2.7 × 10⁸ 4.6 × 10⁷
Cylinder five finger 1.9 × 10⁸ 1.4 × 10⁷

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robotics.sciencemag.org/cgi/content/full/4/26/eaao4900/DC1
MATLAB code (.zip)

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Supplementary Materials

Other Supplementary Material for this manuscript includes the following:

MATLAB code (.zip)

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Adobe PDF - aao4900_SM.pdf

Science Robotics: 4 (26)

Science Robotics

Vol 4, Issue 26
16 January 2019

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Vision-based grasp learning of an anthropomorphic hand-arm system in a synergy-based control framework

By F. Ficuciello, A. Migliozzi, G. Laudante, P. Falco, B. Siciliano

Science Robotics30 Jan 2019

A reinforcement learning algorithm associates postural synergies to geometric object features for stable grasps of unknown objects.

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Vision-based grasp learning of an anthropomorphic hand-arm system in a synergy-based control framework

By F. Ficuciello, A. Migliozzi, G. Laudante, P. Falco, B. Siciliano

Science Robotics30 Jan 2019

A reinforcement learning algorithm associates postural synergies to geometric object features for stable grasps of unknown objects.

Supplementary Materials
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