6,778 research outputs found
The Anthropomorphic Hand Assessment Protocol (AHAP)
The progress in the development of anthropomorphic hands for robotic and prosthetic applications has not been followed by a parallel development of objective methods to evaluate their performance. The need for benchmarking in grasping research has been recognized by the robotics community as an important topic. In this study we present the Anthropomorphic Hand Assessment Protocol (AHAP) to address this need by providing a measure for quantifying the grasping ability of artificial hands and comparing hand designs. To this end, the AHAP uses 25 objects from the publicly available Yale-CMU-Berkeley Object and Model Set thereby enabling replicability. It is composed of 26 postures/tasks involving grasping with the eight most relevant human grasp types and two non-grasping postures. The AHAP allows to quantify the anthropomorphism and functionality of artificial hands through a numerical Grasping Ability Score (GAS). The AHAP was tested with different hands, the first version of the hand of the humanoid robot ARMAR-6 with three different configurations resulting from attachment of pads to fingertips and palm as well as the two versions of the KIT Prosthetic Hand. The benchmark was used to demonstrate the improvements of these hands in aspects like the grasping surface, the grasp force and the finger kinematics. The reliability, consistency and responsiveness of the benchmark have been statistically analyzed, indicating that the AHAP is a powerful tool for evaluating and comparing different artificial hand designs
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
For humans, the process of grasping an object relies heavily on rich tactile
feedback. Most recent robotic grasping work, however, has been based only on
visual input, and thus cannot easily benefit from feedback after initiating
contact. In this paper, we investigate how a robot can learn to use tactile
information to iteratively and efficiently adjust its grasp. To this end, we
propose an end-to-end action-conditional model that learns regrasping policies
from raw visuo-tactile data. This model -- a deep, multimodal convolutional
network -- predicts the outcome of a candidate grasp adjustment, and then
executes a grasp by iteratively selecting the most promising actions. Our
approach requires neither calibration of the tactile sensors, nor any
analytical modeling of contact forces, thus reducing the engineering effort
required to obtain efficient grasping policies. We train our model with data
from about 6,450 grasping trials on a two-finger gripper equipped with GelSight
high-resolution tactile sensors on each finger. Across extensive experiments,
our approach outperforms a variety of baselines at (i) estimating grasp
adjustment outcomes, (ii) selecting efficient grasp adjustments for quick
grasping, and (iii) reducing the amount of force applied at the fingers, while
maintaining competitive performance. Finally, we study the choices made by our
model and show that it has successfully acquired useful and interpretable
grasping behaviors.Comment: 8 pages. Published on IEEE Robotics and Automation Letters (RAL).
Website: https://sites.google.com/view/more-than-a-feelin
Densely Supervised Grasp Detector (DSGD)
This paper presents Densely Supervised Grasp Detector (DSGD), a deep learning
framework which combines CNN structures with layer-wise feature fusion and
produces grasps and their confidence scores at different levels of the image
hierarchy (i.e., global-, region-, and pixel-levels). % Specifically, at the
global-level, DSGD uses the entire image information to predict a grasp. At the
region-level, DSGD uses a region proposal network to identify salient regions
in the image and predicts a grasp for each salient region. At the pixel-level,
DSGD uses a fully convolutional network and predicts a grasp and its confidence
at every pixel. % During inference, DSGD selects the most confident grasp as
the output. This selection from hierarchically generated grasp candidates
overcomes limitations of the individual models. % DSGD outperforms
state-of-the-art methods on the Cornell grasp dataset in terms of grasp
accuracy. % Evaluation on a multi-object dataset and real-world robotic
grasping experiments show that DSGD produces highly stable grasps on a set of
unseen objects in new environments. It achieves 97% grasp detection accuracy
and 90% robotic grasping success rate with real-time inference speed
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