Neural Network Gripper Control Using 2D Images for Precise Object Positioning

The patent replaces complex mechanical sensing systems (depth sensors, stereo cameras, LiDAR) with a simple 2D camera combined with neural network-based image processing. The neural network analyzes 2D images to infer 3D spatial relationships and gripper positioning, substituting physical sensing complexity with computational intelligence.

The patent introduces an intermediary neural network processing system between the simple camera and the gripping control. This intermediary layer processes 2D images to generate movement and rotation information, acting as a mediator that bridges the gap between simple sensing and complex gripping tasks without requiring complex sensors.

The patent employs a inexpensive 2D camera instead of expensive specialized sensors (depth sensors, stereo cameras, LiDAR). While the camera itself is simple and low-cost, the neural network processing provides the necessary intelligence to achieve accurate gripping, effectively replacing expensive sensing hardware with affordable imaging hardware plus software intelligence.

The patent substitutes expensive mechanical/optical sensing systems with a computationally-based solution using a simple camera and neural network. This replacement achieves similar or better performance while dramatically reducing hardware cost.

The patent performs preliminary action by pre-training the neural network on diverse object images and gripper positions. During actual operation, the already-trained neural network directly processes images to generate accurate movement and rotation commands, eliminating the need for real-time trial and error attempts.

The patent implements feedback by continuously capturing images with the camera, processing them through the neural network to generate movement/rotation information, executing the commands, and repeating the cycle. This closed-loop feedback system enables accurate and adaptive gripping without trial-and-error waste.