7,035 research outputs found
Data-Driven Grasp Synthesis - A Survey
We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
Analysis and Observations from the First Amazon Picking Challenge
This paper presents a overview of the inaugural Amazon Picking Challenge
along with a summary of a survey conducted among the 26 participating teams.
The challenge goal was to design an autonomous robot to pick items from a
warehouse shelf. This task is currently performed by human workers, and there
is hope that robots can someday help increase efficiency and throughput while
lowering cost. We report on a 28-question survey posed to the teams to learn
about each team's background, mechanism design, perception apparatus, planning
and control approach. We identify trends in this data, correlate it with each
team's success in the competition, and discuss observations and lessons learned
based on survey results and the authors' personal experiences during the
challenge
- …