Automatic robot perception programming by imitation learning

This invention describes a system for teaching robots to understand and react to their surroundings by watching a human perform a task. It works by identifying specific flat surfaces in what the robot "sees" during the demonstration, calculating how important those surfaces are, and then mapping out a path for the robot to follow. The system then generates variations of this path and uses signals from different types of neural networks to help the robot learn to imitate the human's actions, adjusting its learning based on how much its actions deviate from the planned paths.

Why it matters: Filed before the rapid advancements in multimodal AI and general-purpose foundation models. These developments could significantly enhance the integration of different data modalities and the complex perception and learning tasks described.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedHeavy software system, neural networks

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