Adaptive perception for industrial robotic systems

This invention describes a system for industrial robots in warehouses to improve their item-lifting performance. If a robot fails too often, a smart computer program analyzes environmental conditions, failure types, and item details to figure out why. It then automatically adjusts the robot's control settings to reduce future failures and optimize its lifting operations.

Why it matters: Filed before the widespread maturity of robust, real-time machine learning for robotic control and perception. Today, advancements in ML frameworks and edge computing make it more feasible to deploy adaptive models that learn from diverse operational data to optimize robot performance on the fly.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedRobotic system, ML model, control software

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