Presenting a hierarchical framework to learn semantic-aware locomotion skills for off-road locomotion. Using less than 30 minutes of human demonstration data, the framework learns to adjust the speed and gait of the robot based on the perceived semantics of the environment.
In “Learning Semantics-Aware Locomotion Skills from Human Demonstrations”, Google AI design a hierarchical learning framework to improve a robot’s ability to traverse complex, off-road environments. Unlike previous approaches that focus on environment geometry, such as terrain shape and obstacle locations, Google AI focus on environment semantics, such as terrain type (grass, mud, etc.) and contact properties, which provide a complementary set of information useful for off-road environments.
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