Robotic Learning Apparatus Using Neural Network Evolution
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Solution Overview
Problem
Existing robotic devices require substantial programming efforts for desired functionality and have limited versatility, with pre-programmed robots unable to evolve or adapt new traits, limiting their educational and functional diversity.
Innovation Solution
A robotic apparatus utilizing artificial neural networks with spiking neurons that can learn and evolve by combining configuration vectors from parent networks, allowing for the reproduction and modification of robotic brains through a cloud server system, enabling diverse trait development and adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pre-programmed functions are used in robotic devices, then the device complexity is reduced and ease of manufacture is improved, but the adaptability and versatility of the robot are limited
Solution Approach 1:
The patent implements dynamic reconfiguration of neural network architectures in robotic devices, allowing the system to transition from static pre-programmed functions to dynamically adaptable behaviors. The neural networks can be trained, evolved, and modified during operation, enabling the robot to adapt to new tasks and environments while maintaining a standardized hardware platform that is easy to manufacture.
Solution Approach 2:
The system changes the parameters of the robotic system by storing and loading different neural network configurations (weight matrices, bias vectors, activation functions) that define various behaviors and functionalities. This allows a single robot hardware design to exhibit diverse behaviors by simply changing the software parameters through neural network evolution and learning processes.
2Adaptability or versatility
If substantial programming efforts are required for desired functionality, then the adaptability and versatility of the robot are improved, but the ease of operation and accessibility for users deteriorate
Solution Approach 1:
The patent implements self-service through autonomous neural network learning and evolution processes. The robotic system can automatically train, optimize, and evolve its own neural networks without requiring extensive manual programming by users. The system performs self-learning through reinforcement learning, genetic algorithms, and other automated training methods, making advanced adaptive capabilities accessible to non-expert users.
Solution Approach 2:
The system incorporates feedback mechanisms where the neural networks receive performance feedback from sensors and actuators, allowing them to automatically adjust and improve their behaviors through continuous learning. This feedback-driven approach enables the robot to develop desired functionalities autonomously without requiring users to manually program complex behaviors.
3Device complexity
If a predetermined set of operations is implemented, then the device complexity is reduced, but the productivity and innovation capacity of the robotic system are limited
Solution Approach 1:
The patent implements a universal neural network platform that can perform multiple functions and operations through a single standardized architecture. The same hardware platform can execute diverse tasks by loading different trained neural network models, eliminating the need for complex specialized hardware for each function while maintaining high productivity through efficient neural network inference and execution.
Data Source
AI summary
Apparatus and methods for implementing robotic learning and evolution. An ecosystem of robots may comprise robotic devices of one or more types utilizing artificial neuron networks for implementing learning of new traits. A number of robots of one or more species may be contained in an enclosed environment. The robots may interact with objects within the environment and with one another, while being observed by the human audience. In one or more implementations, the robots may be configured to ‘reproduce’ via duplication, copy, merge, and/or modification of robotic. The replication process may employ mutations. Probability of reproduction of the individual robots may be determined based on the robot's success in whatever function trait or behavior is desired. User-driven evolution of robotic species may enable development of a wide variety of new and/or improved functionality and provide entertainment and educational value for users.