The 3D shape of a robot’s end-effector plays a critical role in determining it’s functionality and overall performance. Many of today’s industrial applications rely on highly customized gripper design for a given task to ensure the system’s robustness and accuracy. However, the process of manual hardware design is both costly and time-consuming, and the quality of the design is also dependent on the engineer’s experience and domain expertise, which can easily be out-dated or inaccurate. The goal of this paper is to use machine learning algorithms to automate this design process and generate task-specific gripper designs that satisfy a set of pre-defined design objectives. We model the design objectives by training a Fitness network to predict their values for a pair of gripper fingers and a grasp object. This Fitness network is then used to provide training supervision to a 3D Generative network that produces a pair of 3D finger geometries for the target grasp object. Our experiments demonstrate that the proposed 3D generative design framework generates parallel jaw gripper finger shapes that achieve more stable and robust grasps as compared to other general-purpose and task-specific gripper design algorithms.
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Grasping Objects Big and Small: Human Heuristics Relating Grasp-Type and Object Size
This paper presents an online data collection
method that captures human intuition about what grasp types
are preferred for different fundamental object shapes and
sizes. Survey questions are based on an adopted taxonomy
that combines grasp pre-shape, approach, wrist orientation,
object shape, orientation and size which covers a large swathe
of common grasps. For example, the survey identifies at
what object height or width dimension (normalized by robot
hand size) the human prefers to use a two finger precision
grasp versus a three-finger power grasp. This information is
represented as a confidence-interval based polytope in the object
shape space. The result is a database that can be used to
quickly find potential pre-grasps that are likely to work, given
an estimate of the object shape and size.
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- Award ID(s):
- 1730126
- NSF-PAR ID:
- 10097549
- Date Published:
- Journal Name:
- IEEE International Conference on Robotics and Automation
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/icra.2018.8460860
