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Robots

Struggling with no matches on Tinder? Embrace a robot! According to research, it’s nearly as satisfying as actual human interaction.

Science has shown that physical touch, even with a robot, has health benefits, according to a review and analysis published in Nature Human Behaviour. The study included a comprehensive review and meta-analysis of 212 studies involving 12,966 individuals and intended on ascertaining the health advantages of touch. The findings showed that physical contact with humans,…

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Reprogramming domestic robots to possess a certain degree of common sense.

Robots are becoming increasingly adept at handling complex household tasks, from cleaning messes to serving meals. However, their ability to handle unexpected disturbances or difficulties during these tasks has been a challenge. Common scenarios like a nudge or a slight mistake that deviates the robot from its expected path can cause the robot to restart…

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A fresh approach relies on collective input from the public to assist in the education of robots.

A team of researchers from MIT, Harvard University, and the University of Washington have developed a novel reinforcement learning technique using crowdsourced feedback. The technique allows AI to learn complex tasks more quickly and without relying on an expertly designed reward function. The conventional reward function designed by dedicated human experts has been replaced by…

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A novel approach employs collective public input to assist in educating robots.

Reinforcement learning, which involves teaching an AI agent a new task using a trial and error methodology, often requires the assistance of a human expert to create and modify the reward function. However, this can be time-consuming, inefficient and difficult to upscale, particularly when the task is highly complex and involves several stages. In response…

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A novel approach incorporates feedback from the public to assist in teaching robots.

Researchers from MIT, Harvard, and the University of Washington have developed a new method for training AI agents using reinforcement learning. Their approach replaces a process often involving a time-consuming design of a reward function by a human expert with feedback crowdsourced from non-expert users. Traditionally, AI reinforcement learning has used a reward function, designed by…

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This fresh approach leverages input from the masses to assist in educating robots.

Teaching AI agents new tasks can be a challenging and time-consuming process, often involving iteratively updating a reward function designed by a human expert to motivate the AI’s exploration of possible actions. However, researchers from the Massachusetts Institute of Technology, Harvard University, and the University of Washington have developed a new reinforcement learning approach that…

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A novel approach uses collective user feedback to assist in the training of robots.

Researchers at MIT, Harvard, and the University of Washington have shunned traditional reinforcement learning approaches, using crowdsourced feedback to teach artificial intelligence (AI) new skills instead. Traditional methods to teach AI tasks often required a reward function, which was updated and managed by a human expert. This limited scalability and was often time-consuming, particularly if…

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A new AI model has the potential to enhance efficiency in robotic warehouses.

MIT researchers have developed a deep-learning model to help robots navigate crowded warehouses, where congestion can slow operations and even lead to crashes. The model does this by dividing the robots into smaller groups and using a path-finding algorithm to decongest each group more quickly. Researchers described the process as being similar to mitigating traffic…

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