Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems

作者：Clemens Eppner, Sebastian Höfer, Rico Jonschkowski, Roberto Martín-Martín, Arne Sieverling, Vincent Wall, Oliver Brock · 年份：2016 · DOI：10.15607/rss.2016.xii.036 · 被引用次数：140 · 研究领域：Robot Manipulation and Learning、Modular Robots and Swarm Intelligence、Reinforcement Learning in Robotics

We describe the winning entry to the Amazon Picking Challenge.From the experience of building this system and competing in the Amazon Picking Challenge, we derive several conclusions: 1) We suggest to characterize robotic system building along four key aspects, each of them spanning a spectrum of solutions-modularity vs. integration, generality vs. assumptions, computation vs. embodiment, and planning vs. feedback.2) To understand which region of each spectrum most adequately addresses which robotic problem, we must explore the full spectrum of possible approaches.To achieve this, our community should agree on key aspects that characterize the solution space of robotic systems.3) For manipulation problems in unstructured environments, certain regions of each spectrum match the problem most adequately, and should be exploited further.This is supported by the fact that our solution deviated from the majority of the other challenge entries along each of the spectra.