Analysis and Observations from the First Amazon Picking Challenge

TL;DR

This paper reviews the first Amazon Picking Challenge, analyzing team surveys and experiences to identify trends, challenges, and lessons learned in developing autonomous warehouse robots.

Contribution

It provides a comprehensive overview of the challenge, survey insights, and observations that highlight current capabilities and future directions for robotic picking systems.

Findings

Identified key factors influencing team success

Highlighted common design and perception approaches

Discussed challenges faced by participating teams

Abstract

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.