2 research outputs found
A Benchmarking Study on Vision-Based Grasp Synthesis Algorithms
In this paper, we present a benchmarking study of vision-based grasp
synthesis algorithms, each with distinct approaches, and provide a comparative
analysis of their performance under different experimental conditions. In
particular, we compare two machine-learning-based and two analytical algorithms
to determine their strengths and weaknesses in different scenarios. In
addition, we provide an open-source benchmarking tool developed from
state-of-the-art benchmarking procedures and protocols to systematically
evaluate different grasp synthesis algorithms. Our findings offer insights into
the performance of the evaluated algorithms, which can aid in selecting the
most appropriate algorithm for different scenarios.Comment: Submitted to International Symposium on Experimental Robotics (ISER)
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