VR Robot Control Training for Autonomous Task Adaptation
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Solution Overview
Problem
Robotic systems face challenges in autonomously reacting to diverse environments due to the complexity of programming them to mimic human-like task execution and adaptability, as they often lack effective utilization of human-like control information and operator interface sensing capabilities.
Innovation Solution
A method is described for deriving autonomous control information by receiving environment sensor data and device control instructions, allowing robotic systems to simulate human-like actions and adapt to environments, enabling them to take autonomous actions through the generation of autonomous device control signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robotic systems are programmed to autonomously react to diverse environments, then adaptability improves, but device complexity and programming difficulty increase significantly
Solution Approach 1:
The patent copies human operator behavior patterns and decision-making processes into the robotic system. By observing and replicating how human operators control devices in various environments, the system learns adaptive behaviors without requiring complex explicit programming for each scenario. The virtual reality interface further copies the human operator's sensory and motor experiences to train the autonomous control system.
Solution Approach 2:
The system performs preliminary learning and adaptation through virtual reality simulations before actual autonomous operation. Operators train in virtual environments first, allowing the system to pre-learn appropriate responses to various situations. This preliminary training phase reduces the complexity of real-time autonomous decision-making by establishing behavioral patterns in advance.
2Manufacturing precision
If robotic systems use extensive programming to execute tasks, then task execution precision improves, but loss of time and operational efficiency worsen
Solution Approach 1:
The robotic system performs self-learning and self-programming by observing operator behavior and autonomously generating control algorithms. Instead of requiring extensive manual programming, the system serves itself by automatically acquiring task execution knowledge from virtual reality training data, significantly reducing programming time while maintaining precision through iterative learning.
Solution Approach 2:
The system implements continuous feedback loops where operator performance in virtual reality environments is analyzed and used to refine autonomous control algorithms. This feedback mechanism allows the system to improve task execution precision over time without requiring additional programming effort, as the learning process automatically optimizes performance based on observed outcomes.
3Ease of operation
If operator interfaces are designed to sense operator movements, then ease of operation improves, but device complexity increases
Solution Approach 1:
The virtual reality interface serves multiple functions simultaneously: it provides immersive environmental representation, tracks operator movements and actions, captures sensory information, and generates training data for autonomous learning. By consolidating these diverse functions into a single integrated system, the interface achieves high ease of operation without proportionally increasing complexity, as each component serves multiple purposes.
Data Source
AI summary
A method of deriving autonomous control information involves receiving one or more sets of associated environment sensor information and device control instructions. Each set of associated environment sensor information and device control instructions includes environment sensor information representing an environment associated with an operator controllable device and associated device control instructions configured to cause the operator controllable device to simulate at least one action taken by at least one operator experiencing a representation of the environment generated from the environment sensor information. The method also involves deriving autonomous control information from the one or more sets of associated environment sensor information and device control instructions, the autonomous control information configured to facilitate generating autonomous device control signals from autonomous environment sensor information representing an environment associated with an autonomous device, the autonomous device control signals configured to cause the autonomous device to take at least one autonomous action.