1,468 research outputs found
A Certified-Complete Bimanual Manipulation Planner
Planning motions for two robot arms to move an object collaboratively is a
difficult problem, mainly because of the closed-chain constraint, which arises
whenever two robot hands simultaneously grasp a single rigid object. In this
paper, we propose a manipulation planning algorithm to bring an object from an
initial stable placement (position and orientation of the object on the support
surface) towards a goal stable placement. The key specificity of our algorithm
is that it is certified-complete: for a given object and a given environment,
we provide a certificate that the algorithm will find a solution to any
bimanual manipulation query in that environment whenever one exists. Moreover,
the certificate is constructive: at run-time, it can be used to quickly find a
solution to a given query. The algorithm is tested in software and hardware on
a number of large pieces of furniture.Comment: 12 pages, 7 figures, 1 tabl
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
Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packing
Robotic picking from cluttered bins is a demanding task, for which Amazon
Robotics holds challenges. The 2017 Amazon Robotics Challenge (ARC) required
stowing items into a storage system, picking specific items, and packing them
into boxes. In this paper, we describe the entry of team NimbRo Picking. Our
deep object perception pipeline can be quickly and efficiently adapted to new
items using a custom turntable capture system and transfer learning. It
produces high-quality item segments, on which grasp poses are found. A planning
component coordinates manipulation actions between two robot arms, minimizing
execution time. The system has been demonstrated successfully at ARC, where our
team reached second places in both the picking task and the final stow-and-pick
task. We also evaluate individual components.Comment: In: Proceedings of the International Conference on Robotics and
Automation (ICRA) 201
- …