Professor Chelsea Finn and researchers from Shanghai Qi Zhi Institute have developed a new vision-based algorithm that helps robodogs scale high objects, leap across gaps, crawl under thresholds, and squeeze through crevices – and then bolt to the next challenge. The algorithm represents the brains of the robodog.

"The autonomy and range of complex skills that our quadruped robot learned is quite impressive," said Chelsea, senior author of a new peer-reviewed paper announcing the teams' approach to the world, which will be presented at the upcoming Conference on Robot Learning. "And we have created it using low-cost, off-the-shelf robots – actually, two different off-the-shelf robots."

The key advance, the authors say, is that their robodog is autonomous – that is, it is able to size up physical challenges and imagine, then execute, a broad range of agility skills based simply on the obstacles it sees before it.

"What we're doing is combining both perception and control, using images from a depth camera mounted on the robot and machine learning to process all those inputs and move the legs in order to get over, under, and around obstacles," said Zipeng Fu, a doctoral candidate in Chelsea’s IRIS Lab and first author of the study, along with Ziwen Zhuang of Shanghai Qi Zhi Institute.

Excerpted from Stanford News, 'New dog, old tricks: New AI approach yields ‘athletically intelligent’ robotic dog.'