PFI:AIR - TT: High-Reliability Robot Grasping for Per-Item Distribution

Harvard University, Cambridge MA

Investigators

Abstract

This PFI:AIR Technology Translation project is aimed at a proof-of-concept demonstration of a robotic grasping system with an error rate of 1 in 10,000 trials or better. In prior NSF-funded work, the researchers developed highly capable, low-cost robot hands which, when paired with a standard industrial robot arm and vision system, can grasp a sufficiently wide range of items to be useful for e-commerce order fulfillment and automated restocking applications. The intended applications, however, require extremely low error rates. This project will (1) develop a system to independently test the reliability of the grasping system on a realistic variety of potential customer inventory, and (2) implement and evaluate new mechanisms for the system to detect and correct errors during grasping. The proposed in-depth evaluation of the reliability of grasping systems in real-world tasks is unprecedented. To date, published assessments of grasp system success is limited to a few dozen to a few hundred trials - and typically results in error rates of 1-10%. The proposed methods for evaluation of grasp errors represents an essential capability for validating the performance of robot hands as they move out of laboratories and factories into diverse real-world settings like homes, hospitals, and shop floors. In addition, this project will develop new methods for independently assessing errors in grasping, as well as new methods for enhancing the reliability of grasping. The proposed system will produce very large data sets that will be exploited in future work for data-driven learning of grasp control and on-line error detection and correction. In addition, post docs involved in this project will receive entrepreneurship and technology translation experiences through working to define customer needs and developing application-driven technology. High-reliability automated grasping systems promise to reduce costs and enhance the productivity of the warehousing and logistics businesses that are a rapidly-growing segment of the economy. By automating the selection of inventory from automated storage and retrieval systems, tasks such as order picking, auditing and packing can be accomplished with fewer sources of error, which is crucial in handling high-value products such as pharmaceuticals and electronics. The development of systems and methods for assessing grasping success for diverse object sets will lay the essential groundwork for many imminent real-world applications of grasping in less structured environments.