Adaptive Robot Predictor Control for Autonomous Task Learning
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Solution Overview
Problem
Existing robotic devices face challenges in adaptability and efficiency due to the need for costly programming and reliance on human operators for control, which can be inadequate in dynamic environments, requiring frequent code changes and user experience.
Innovation Solution
An adaptive computerized predictor apparatus using a learning process based on sensory context and teaching input, employing a network of computerized neurons with adaptable connection efficacies to predict control outputs, enabling autonomous actions such as obstacle avoidance and target approach in robotic platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robotic devices are programmed to perform desired functionality, then reliability is improved, but device complexity and cost increase
Solution Approach 1:
The robotic device performs self-learning through a learning process that automatically adjusts internal parameters based on sensory inputs and desired outcomes, eliminating the need for external programming. The system serves itself by autonomously adapting its control algorithms through iterative learning from environmental feedback.
Solution Approach 2:
The learning process dynamically changes internal parameters of the robotic device based on sensory context and teaching inputs. These parameter adjustments occur automatically during operation, allowing the device to adapt its behavior without modifying its structural complexity or programming code.
2Adaptability or versatility
If robotic devices are remotely controlled by humans, then adaptability is improved, but loss of time and productivity decrease
Solution Approach 1:
The robotic device autonomously processes sensory inputs and generates appropriate responses without requiring human operator intervention. The self-learning capability enables the system to independently adapt to changing environmental conditions, eliminating time loss associated with human control.
Solution Approach 2:
The system continuously receives feedback from sensory inputs and uses this information to adjust its behavior in real-time. This closed-loop feedback mechanism enables rapid adaptation to dynamic environments without the delays inherent in human-operated systems.
3Adaptability or versatility
If programming code is changed to adapt to model or environment changes, then adaptability is improved, but device complexity and loss of time increase
Solution Approach 1:
Instead of modifying programming code, the system adapts by dynamically changing its internal parameters through the learning process. This approach maintains code stability while achieving environmental adaptation through parameter optimization based on sensory feedback.
Solution Approach 2:
The robotic device performs self-adjustment of its operational parameters without external intervention. The learning algorithm automatically modifies internal settings in response to environmental changes, eliminating the need for programmers to update the code.
4Ease of operation
If extensive programming is used to enable autonomous operation, then ease of operation is improved, but device complexity and cost increase
Solution Approach 1:
The robotic device achieves autonomous operation through self-learning rather than pre-programming. The system automatically develops operational capabilities by processing sensory inputs and adjusting its behavior, eliminating the need for extensive programming while maintaining ease of operation.
Solution Approach 2:
The learning process performs preliminary adaptation during operation, allowing the robotic device to develop autonomous capabilities in real-time. This approach replaces the need for extensive pre-programming with on-the-fly parameter optimization.
Data Source
AI summary
Apparatus and methods for training and operating of robotic devices. Robotic controller may comprise a predictor apparatus configured to generate motor control output. The predictor may be operable in accordance with a learning process based on a teaching signal comprising the control output. An adaptive controller block may provide control output that may be combined with the predicted control output. The predictor learning process may be configured to learn the combined control signal. Predictor training may comprise a plurality of trials. During initial trial, the control output may be capable of causing a robot to perform a task. During intermediate trials, individual contributions from the controller block and the predictor may be inadequate for the task. Upon learning, the control knowledge may be transferred to the predictor so as to enable task execution in absence of subsequent inputs from the controller. Control output and/or predictor output may comprise multi-channel signals.