For a while, it looked like Rethink Robotics would shake up the world with its collaborative robots: rather than having to write code, workers could teach bots to perform tasks by guiding them through the process. However, the market doesn't appear to have shared its vision. Rethink has suddenly shut down after a potential buyer backed out of a deal. Sales of Baxter and Sawyer robots weren't meeting expectations, Rethink chief Scott Eckert said, leaving the company low on cash. It really needed this acquisition to go through, in other words.
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Figure 2 describes how a robot will function under the safety rated monitored stop standard depending on where the robot is, where the operator is, and if the robot is moving. If the robot is outside the collaborative workspace, it will keep working whether the operator is inside or outside the collaborative workspace. If the robot is inside the collaborative workspace, it will only continue to function while the operator is outside the collaborative workspace. If the operator steps inside, the robot will come to a monitored stop. If the robot is inside the collaborative workspace and has already come to a monitored stop, the operator can be inside or outside the space without any change to the robot's function.
A collaborative robot, also known as a cobot, is a robot that is capable of learning multiple tasks so it can assist human beings. In contrast, autonomous robots are hard-coded to repeatedly perform one task, work independently and remain stationary. Today, advances in mobile technology, machine vision, cognitive computing and touch technology (including collision avoidance) are making it possible for small, lower-power robots to be aware of their surroundings and perform multiple types of tasks safely in close proximity to human workers. A cobot, when working side by side a human, can quickly learn tasks through demonstration and reinforcement learning. As of this writing, the majority of industrial robots are still autonomous.
The primary application of collaborate filtering (CF) is to recommend a small set of items to a user, which entails ranking. Most approaches, however, formulate the CF problem as rating prediction, overlooking the ranking perspective. In this work we present a method for collaborative ranking that leverages the strengths of the two main CF approaches, neighborhood- and model-based. Our novel method is highly efficient, with only seventeen parameters to optimize and a single hyperparameter to tune, and beats the state-of-the-art collaborative ranking methods. We also show that parameters learned on one dataset yield excellent results on a very different dataset, without any retraining.