Amazon Robotics

Amazon Robotics automates fulfillment center operations using various methods of robotic technology including autonomous mobile robots, sophisticated control software, language perception, power management, computer vision, depth sensing, machine learning, object recognition, and semantic understanding of commands.

Research

Faculty Advisor: Amon Millner

Low-Cost Robotics Detection, Classification, and Tracking

Amazon Robotics engaged SCOPE students in exploring the space of classifying living and non-living beings, locating them, and understanding the vectors they’re heading using commercial-grade and consumer-grade sensing systems.

Research

Five SCOPE students in front of poster at Summit

Faculty Advisor: Alessandra Ferzoco

Robotic Manipulation using Tactile Feedback

To help Amazon Robotics' research and development efforts, this year's SCOPE team created a robotic system that utilizes tactile and vision sensors to grasp a ball and throw it to reach a specified target.

Research

Students posing in front of project poster at Summit

Faculty Advisor: Lawrence Neeley

Amazon Object Manipulation Taxonomy

This year’s SCOPE team created an extensible object taxonomy framework and implemented an integrated active perception system to predict the performance of various object gripping strategies. This system can be used to enable a more diverse range of robot picking form factors and increase accuracy in warehouse operations at Amazon.

Research

Faculty Advisor: Alessandra Ferzoco

The Automated Lift Object System

This year's SCOPE team built off a perception system for remote control of a robot developed by last year’s team resulting in increased manipulation efficiency and reduced onboarding duration and complexity for operators.

Research

Faculty Advisor: Alessandra Ferzoco

Amazon Robotics

This year’s SCOPE team created a package manipulation robot, with an integrated perception system, that can be operated remotely. The robot control system can be used to remotely support Amazon associates and improve the ability to complete orders quickly.

Research

Faculty Advisor: Alessandra Ferzoco