Neural Network Robot Control for Unknown Environments
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Solution Overview
Problem
Training robots to perform actions is expensive and requires significant human interaction, and trained robots often need environment information to perform correctly.
Innovation Solution
A neural network training system that uses behavior cloning and a planning module to generate training actions for autonomous devices, which are then observed and imitated by an image sensor to train the neural network without human supervision, allowing the device to perform tasks in unknown environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional robot training methods are used with human operators controlling the robot to perform specific tasks, then the robot can learn through direct human interaction and rewards, but the training process becomes very expensive and time-consuming
Solution Approach 1:
The patent creates virtual copies of the physical environment and robot through simulation. A planning module generates training actions in the virtual environment, and an image sensor captures images of these simulated actions. The neural network is trained on these synthetic images and actions, eliminating the need for expensive and time-consuming real-world human-operated training while maintaining training effectiveness
Solution Approach 2:
The system enables self-supervised learning where the robot trains itself through autonomously generated training data. The planning module automatically generates diverse training scenarios and actions without human intervention, and the neural network learns from these self-generated examples, making the training process independent of human operators
2Reliability
If traditional training methods are used requiring human operators to control and reward the robot, then the robot can learn task performance, but the process requires significant human interaction and resources
Solution Approach 1:
The patent replaces expensive real-world training with cheaper virtual simulations. By copying the physical environment into a simulated version and generating training data through the planning module, the system achieves the same learning objectives at minimal cost, eliminating expenses related to human operators, physical robot wear, and real-world setup
Solution Approach 2:
The system uses disposable synthetic training data generated by the planning module instead of expensive reusable human expertise. Each training iteration generates new synthetic images and actions that can be discarded and replaced with new generated data, eliminating the need for continuous human involvement and reducing overall training costs
3Adaptability or versatility
If trained robots use environment information to perform actions correctly, then the robot can adapt to known environments, but the robot cannot perform tasks in unknown or unseen environments
Solution Approach 1:
The patent performs preliminary training in diverse virtual environments through the planning module, which generates a wide variety of training scenarios including different object configurations, lighting conditions, and spatial arrangements. This preliminary exposure to varied synthetic environments enables the neural network to generalize to unknown real-world environments without requiring environment-specific information during deployment
Solution Approach 2:
The neural network is designed to be universally applicable across different environments by training on diverse synthetic data from the planning module. The system learns environment-agnostic features and relationships that transfer across various settings, enabling the robot to perform tasks in unknown environments using only image input without requiring environment-specific training or information
4Extent of automation
If behavior cloning is used with planning module to generate training actions, then the system can train without human supervision, but the system requires image sensors and neural network infrastructure
Solution Approach 1:
The patent replaces the mechanical system of human-operated physical robot training with an information-based system using image sensors, planning modules, and neural networks. The physical robot training process is substituted with synthetic image generation and neural network learning, achieving higher automation despite increased computational infrastructure requirements
Data Source
AI summary
Apparatuses, systems, and techniques to cause actions to be performed by an autonomous machine in a previously unknown environment. In at least one embodiment, one or more neural networks are trained based, at least in part, on images of one or more automatically generated training actions.