Neural Implicit Value Functions for Real-Time Robot Grasping

The patent combines object detection and motion planning into a unified neural network model that processes sensor data and directly outputs robot joint trajectories. This integration eliminates the sequential two-step approach, allowing simultaneous processing of detection and planning functions, thereby reducing processing time while maintaining grasping reliability for both static and moving objects.

The patent replaces the traditional mechanical control system (separate detection algorithms and motion planning algorithms) with a neural network-based system. The neural network learns optimal trajectories from training data, substituting complex computational mechanics with learned patterns that can be rapidly evaluated during operation, enabling real-time response to moving objects.

The patent performs preliminary training of the neural network offline using extensive simulation data that covers various object positions, orientations, and motion patterns. This pre-computed knowledge is stored in the network weights, allowing the robot to rapidly adapt to moving objects during operation without performing complex real-time computations, thus increasing adaptability while reducing operational computational complexity.

The patent uses simulation environments to create virtual copies of physical scenarios for training the neural network. By learning from these simulated copies of real-world interactions, the network acquires generalized knowledge that transfers to physical robot operation, enabling adaptive response to moving objects without requiring complex real-time sensing and computation for each new situation.