Analysis and Observations From the First Amazon Picking Challenge

TLDR

The task of picking items from warehouse shelves is currently performed by humans, and robots are hoped to improve efficiency, throughput, and reduce costs. The paper overviews the inaugural Amazon Picking Challenge and surveys 26 teams, aiming to design an autonomous robot that picks items from warehouse shelves. The authors surveyed 26 teams with a 28‑question questionnaire covering background, mechanism design, perception, planning, and control, then analyzed trends and correlated them with competition success. The analysis revealed trends linking design choices to competition performance and yielded observations and lessons for future autonomous picking systems.

Abstract

This paper presents an 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.