AR-Enhanced Robot Training via Virtual Sensory Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Training artificial intelligence systems for robots is time-consuming and expensive, especially when simulating rare or impractical events, as traditional methods rely heavily on real-world data and complex simulations.
Innovation Solution
Utilizing augmented reality data, which combines simulation data with sensory data, to train agents and track responses, allowing for the simulation of rare events or impractical scenarios, such as injecting threats into sensory data, to effectively train and validate agents controlling robots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional real-world data collection and complex simulations are used to train AI systems for robots, then training reliability and comprehensiveness are improved, but training time and cost increase significantly
Solution Approach 1:
The patent creates virtual copies of real-world environments, objects, and scenarios through 3D modeling and simulation. These virtual replicas allow AI training without physical resource consumption, enabling comprehensive training scenarios including rare and dangerous events without the time and cost constraints of real-world data collection
Solution Approach 2:
The patent introduces an augmented reality intermediary layer that bridges virtual simulation data and real-world robot operation. This intermediary enables training in virtual environments while maintaining transferability to real-world scenarios, resolving the contradiction between simulation efficiency and real-world reliability
2Reliability
If traditional real-world data collection methods are used, then training data authenticity is improved, but ability to simulate rare and dangerous events deteriorates
Solution Approach 1:
The patent enables preliminary creation of virtual training scenarios including rare and dangerous events before they occur in reality. By pre-configuring virtual environments with hazardous conditions, rare events, and edge cases, the system prepares AI agents for scenarios that would be impractical or unsafe to capture through traditional real-world data collection
Solution Approach 2:
The patent creates virtual copies of real-world scenarios while allowing modification to include rare and dangerous events. These synthetic copies maintain visual and sensory realism through augmented reality techniques while enabling simulation of events that would be impossible or unsafe to replicate physically
3Adaptability or versatility
If complex simulation environments are created to train AI systems, then training scenario comprehensiveness is improved, but system complexity and computational resources increase
Solution Approach 1:
The patent creates a universal virtual training platform that can simulate multiple different environments, scenarios, and conditions within a single system. This multi-functional simulation environment eliminates the need for separate complex simulation systems for each training scenario, reducing overall system complexity while maintaining comprehensiveness
Solution Approach 2:
The patent uses virtual copying techniques to represent complex real-world environments as simplified digital models. These virtual copies capture essential training features while omitting unnecessary physical complexities, enabling comprehensive scenario training with reduced computational overhead
Data Source
AI summary
Substantially as described and illustrated herein including devices, methods of operation for the systems or devices, articles of manufacture including stores processor-executable instructions, and a system including a robot. The system includes at least one processor. The system may further include a nontransitory processor-readable storage device communicatively coupled to at least one processor and which stores processor-executable instructions which, when executed by the at least one processor, cause the at least one processor to composite environment information that represents an environment and virtual item information that represents the virtual item to produce composited information, present to an agent the composited information, and receive action information that represents an action for the robot to perform via the output system.