{"id":558880,"date":"2021-02-09T12:16:10","date_gmt":"2021-02-09T17:16:10","guid":{"rendered":"https:\/\/www.therobotreport.com\/?p=558880"},"modified":"2021-02-09T12:28:51","modified_gmt":"2021-02-09T17:28:51","slug":"shadow-help-robots-understand-human-touch","status":"publish","type":"post","link":"https:\/\/www.therobotreport.com\/shadow-help-robots-understand-human-touch\/","title":{"rendered":"How shadows can help robots understand human touch"},"content":{"rendered":"
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By placing a camera inside a robot, researchers can infer how the person is touching it and what the person\u2019s intent is just by looking at the shadow images. | Credit: Cornell University<\/strong><\/p><\/div>\n

Cornell University researchers created a low-cost method for soft robots<\/a> to detect a range of physical interactions, from pats to punches to hugs, without relying on touch at all. Instead, a USB camera located inside the robot captures the shadow movements of hand gestures on the robot\u2019s skin and classifies them with machine-learning software.<\/p>\n

The new ShadowSense technology is the latest project from Cornell’s Human-Robot Collaboration and Companionship Lab. The group published a paper on the research called “ShadowSense: Detecting Human Touch in a Social Robot Using Shadow Image Classification<\/a>.”<\/p>\n

“Touch is such an important mode of communication for most organisms, but it has been virtually absent from human-robot interaction,” said Guy Hoffman, associate professor at Cornell’s Sibley School of Mechanical and Aerospace Engineering and the paper’s senior author. “One of the reasons is that full-body touch used to require a massive number of sensors, and was therefore not practical to implement. This research offers a low-cost alternative.\u201d<\/p>\n

The technology originated as part of a collaboration to develop inflatable robots that could guide people to safety during emergency evacuations. Such a robot would need to be able to communicate with humans in extreme conditions and environments. Imagine a robot physically leading someone down a noisy, smoke-filled corridor by detecting the pressure of the person\u2019s hand.<\/p>\n

Rather than installing a large number of contact sensors \u2013 which would add weight and complex wiring to the robot, and would be difficult to embed in a deforming skin \u2013 the team took a counterintuitive approach. In order to gauge touch, they looked to sight.<\/p>\n

\u201cBy placing a camera inside the robot, we can infer how the person is touching it and what the person\u2019s intent is just by looking at the shadow images,\u201d said Yuhan Hu, the paper\u2019s lead author. “We think there is interesting potential there, because there are lots of social robots that are not able to detect touch gestures.”<\/p>\n

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