Abstract-- Vision-based Tactile Sensors (VBTSs) show significant promise in that they can leverage image measurements to provide high-spatial-resolution human-like performance. However, current VBTS designs, typically confined to the fingertips of robotic grippers, prove somewhat inadequate, as many grasping and manipulation tasks require multiple contact points with the object. With an end goal of enabling large-scale, multisurface tactile sensing via VBTSs, our research (i) develops a synchronized image acquisition system with minimal latency, (ii) proposes a modularized VBTS design for easy integration into finger phalanges, and (iii) devises a zero-shot calibration approach to improve data efficiency in the simultaneous calibration of multiple VBTSs. In validating the system within a miniature 3-fingered robotic gripper equipped with 7 VBTSs we demonstrate improved tactile perception performance by covering the contact surfaces of both gripper fingers and palm. Additionally, we show that our VBTS design can be seamlessly integrated into various end-effector morphologies significantly reducing the data requirements for calibration.

Automating warehouse operations can reduce logistics overhead costs, ultimately driving down the final price for consumers, increasing the speed of delivery, and enhancing the resiliency to market fluctuations. This extended abstract showcases a large-scale package manipulation from unstructured piles in Amazon Robotics' Robot Induction (Robin) fleet, which is used for picking and singulating up to 6 million packages per day and so far has manipulated over 2 billion packages. It describes the various heuristic methods developed over time and their successor, which utilizes a pick success predictor trained on real production data. To the best of the authors' knowledge, this work is the first large-scale deployment of learned pick quality estimation methods in a real production system.

In 2019, Amazon's facial-recognition technology erroneously identified Duron Harmon of the New England Patriots, Brad Marchand of the Boston Bruins and 25 other New England athletes as criminals when it mistakenly matched the athletes to a database of mugshots. How can artificial intelligence be better, and when will companies and their customers be able to trust it? "The issue of mistrust in AI systems was a major theme at IBM's annual customer and developer conference this year," said Ron Poznansky, who works in IBM design productivity. "To put it bluntly, most people don't trust AI--at least, not enough to put it into production. A 2018 study conducted by The Economist found that 94% of business executives believe that adopting AI is important to solving strategic challenges; however, the MIT Sloan Management Review found in 2018 that only 18% of organizations are true AI'pioneers,' having extensively adopted AI into their offerings and processes. This gap illustrates a very real usability problem that we have in the AI community: People want our technology, but it isn't working for them in its current state."