Robotic Control Model Segmentation for Skill Analysis
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Solution Overview
Problem
Robotic devices face lengthy analysis times when determining skills to execute tasks due to a high number of skills and parameters, requiring efficient methods to ascertain skills quickly and reduce computational effort.
Innovation Solution
A control model that learns from demonstrations, using a trained parameter model and object model to determine the most probable state transitions and task parameters, allowing for rapid skill execution without complex calculations, and can be linearly expanded for additional skills.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a high number of skills and parameters are used to carry out tasks, then the robotic device can perform more complex tasks, but the analysis time for ascertaining skills becomes lengthy
Solution Approach 1:
The control model is segmented into a parameter model and an object model, where the parameter model handles task parameter determination and the object model handles object state probability. This segmentation allows independent optimization of each model, reducing overall computational complexity while maintaining the ability to handle multiple skills and parameters.
Solution Approach 2:
The control model is trained in advance using demonstration data before actual task execution. During training, the model learns the relationships between task parameters, object states, and appropriate skills. This preliminary action enables rapid skill selection during operation without requiring complex real-time analysis, thus reducing analysis time while maintaining versatility.
2Reliability
If traditional skill ascertainment methods are used, then all skills and parameters can be considered, but complex calculations are required and manual selection is needed
Solution Approach 1:
The control model uses probabilistic feedback mechanisms where the object model provides probability distributions for object states, and the parameter model uses this feedback to determine optimal task parameters. This feedback loop enables automated skill selection based on learned patterns from demonstrations, eliminating manual selection while maintaining reliability through probabilistic reasoning.
Solution Approach 2:
The patent replaces traditional mechanical skill selection methods (manual selection and complex calculations) with a learned control model that uses probabilistic inference. The model substitutes complex deterministic calculations with probabilistic predictions based on trained parameters, reducing computational complexity while maintaining or improving selection accuracy.
3Adaptability or versatility
If more skills are added to the robotic device, then the device becomes more versatile, but the computational setup and time expenditure increase
Solution Approach 1:
The control model is designed as a universal framework that can handle any number of skills and parameters through a unified probabilistic approach. The parameter model and object model work together in a generalizable architecture that scales with the number of skills without requiring separate processing for each skill, thus maintaining computational efficiency while increasing versatility.
Solution Approach 2:
The patent uses parameter changes in the probabilistic models to adapt to different numbers of skills and parameters. By adjusting the parameters of the control model during training, the system can efficiently handle varying levels of complexity without increasing computational setup or time expenditure during operation. The learned parameters encode the relationships for all skills, enabling efficient skill selection regardless of the total number of skills available.
Data Source
AI summary
A device and method for controlling a robotic device. The method includes: training a control model, which includes a parameter model and an object model, including: providing for each initial state-target state pair of a plurality of initial state-target state pairs a control state sequence, including states and transition states, each transition state being assigned a set of task parameters; ascertaining a set of state transition-state-state transition triples, and for each: adapting the parameter model so that the parameter model ascertains a probability distribution for each task parameter from the set of task parameters, which is assigned to the state transition following the state, adapting the object model so that the object model ascertains for each object a probability distribution for the state of the object; and controlling the robotic device with the control model using the trained parameter model and the trained object model.