Obstacle avoidance by a drone (VIDEO)
Caption
Princeton researchers adapted machine learning frameworks from other arenas to the field of robot locomotion and manipulation, applying generalization theory to the complex task of making guarantees on robots' performance in unfamiliar settings. In a proof of principle, the researchers validated the technique by assessing the obstacle avoidance of a small drone called a Parrot Swing as it flew down a 60-foot-long corridor dotted with cardboard cylinders. The guaranteed success rate of the drone's control policy was 88.4%, and it avoided obstacles in 18 of 20 trials (90%).
Credit
Intelligent Robot Motion Lab at Princeton University
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