Training artificial networks for robotic picking
AssigneeEmbodied Intelligence Inc.
InventorsYan Duan, Haoran Tang, Yide Shentu, Nikhil Mishra, Xi Chen
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AssigneeEmbodied Intelligence Inc.
InventorsYan Duan, Haoran Tang, Yide Shentu, Nikhil Mishra, Xi Chen
Estimating the term…
Various embodiments of the present technology generally relate to robotic devices and artificial intelligence. More specifically, some embodiments relate to an artificial neural network training method that does not require extensive training data or time expenditure. The few-shot training model disclosed herein includes attempting to pick up items and, in response to a failed pick up attempt, transferring and generalizing information to similar regions to improve probability of success in future attempts. In some implementations, the training method is used to robotic device for picking items from a bin and perturbing items in a bin. When no picking strategies with high probability of success exist, the robotic device may perturb the contents of the bin to create new available pick-up points. In some implementations, the device may include one or more computer-vision systems.
A method of training an artificial neural network, the method comprising:
A system comprising:
Dependencies are inferred from the claim text.