Exploiting Human Motor Skills for Training Bipedal Robots Undergraduate Honors Thesis
نویسنده
چکیده
Although machine learning, reinforcement learning, and learning from demonstration have improved the rate and accuracy at which robots can gain intelligence from humans, they haven’t reached the rapid rate at which humans are able to acquire new knowledge. Many systems that exploit imitation learning use simple positive and negative reinforcement, and place the burden of learning completely on the computer. This neglects the expressive capabilities of humans, as well as their remarkable ability to quickly refine motor skills. While passive dynamics offers the most human-like locomotion for bipedal robots, it also relies on particular design specifications. This thesis presents a general Framework for Interactive Control of a Humanoid by Motion Capture (FICHMC ), that offers rapid motion development for large classes of bipedal robots. Essentially, a human in a motioncapture laboratory “puppets” a biped, with a real-time mapping from human to robot. The training process requires no technical knowledge and provides a natural interface for humans to directly transfer skills to robots.
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