Robotic Training Apparatus Adaptive Controller
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing robotic devices face challenges in adaptability and efficiency due to costly programming and reliance on human operators for control, especially when environmental or model changes occur rapidly, leading to inadequate performance in dynamic situations.
Innovation Solution
A computerized controller apparatus that uses processors to execute modules for training robotic devices through iterative trials, determining control signals based on input characteristics and error measures to improve action precision over time, allowing for autonomous learning and reduced human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If robotic devices are programmed to perform desired functionality, then manufacturing precision and reliability are improved, but device complexity and cost increase
Solution Approach 1:
The robotic device performs self-training by executing trials autonomously, collecting performance data, and adjusting its own control parameters without external intervention. The processor executes training modules that enable the device to learn optimal control signals through iterative trials, reducing the need for complex external programming while improving action precision through self-optimized performance measures
Solution Approach 2:
The system performs preliminary training trials before actual operation to establish optimal control parameters. During these training trials, the robotic device executes multiple iterations of actions, measures performance, and adjusts control signals in advance, so that when deployed for actual tasks, the device already possesses optimized control parameters that improve precision without requiring complex real-time programming
2Adaptability or versatility
If robotic devices are remotely controlled by humans, then adaptability to new situations is improved, but loss of time and productivity decrease
Solution Approach 1:
The system implements feedback loops where the robotic device continuously monitors its own performance measures during training trials and uses this feedback to adjust control parameters. The processor compares actual performance against target performance and modifies control signals accordingly, enabling the device to adapt to changes autonomously without requiring human intervention while maintaining rapid response times
Solution Approach 2:
The control parameters and performance measures are dynamically adjusted during training trials based on real-time performance data. The system transitions from static pre-programmed controls to dynamic adaptive controls that evolve during operation, allowing the robotic device to respond quickly to environmental changes and optimize its performance through iterative learning
3Manufacturing precision
If extensive programming is used to control robotic devices, then manufacturing precision is improved, but ease of operation and adaptability worsen
Solution Approach 1:
The robotic device autonomously performs training trials and self-adjusts control parameters without requiring extensive external programming or manual configuration. The processor executes training modules that enable the device to learn optimal performance through self-directed experimentation, making the system easy to operate while achieving high precision through autonomous learning
Solution Approach 2:
The system optimizes control parameters dynamically during training trials by adjusting performance measures and control signals based on measured outcomes. Rather than requiring fixed pre-programmed parameters, the system changes parameters adaptively during operation to achieve optimal precision, simplifying operation while maintaining high performance
Data Source
AI summary
Apparatus and methods for training of robotic devices. Robotic devices may be trained by a user guiding the robot along target trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control commands based on one or more of the user guidance, sensory input, and/or performance measure. Training may comprise a plurality of trials. During first trial, the user input may be sufficient to cause the robot to complete the trajectory. During subsequent trials, the user and the robot's controller may collaborate so that user input may be reduced while the robot control may be increased. Individual contributions from the user and the robot controller during training may be may be inadequate (when used exclusively) to complete the task. Upon learning, user's knowledge may be transferred to the robot's controller to enable task execution in absence of subsequent inputs from the user.