Discrepancy Detection for Adaptive Robotic Learning
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Solution Overview
Problem
Existing robotic devices require costly programming and human operator control, and changes in robot models or environments necessitate changes in programming code, making them inefficient for adaptive tasks.
Innovation Solution
A robotic apparatus comprising a platform, sensor module, and controller that executes actions based on environmental information, determines predicted and actual outcomes, and adjusts actions to achieve target tasks through a discrepancy signal, enabling adaptive learning and autonomous operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robotic devices are programmed to perform desired functionality, then they can accomplish specific tasks, but changes in robot model or environment require changes in programming code, reducing adaptability
Solution Approach 1:
The robotic device performs self-training by autonomously exploring its environment, collecting sensory data, and updating its own neural network parameters without external intervention. The device generates its own training data through autonomous exploration and uses discrepancy detection to identify learning opportunities, enabling it to adapt to new environments and tasks without requiring reprogramming by humans.
Solution Approach 2:
The system performs preliminary training actions by exploring the environment beforehand to build internal models of physical dynamics and task requirements. Through preliminary exploration and discrepancy detection, the robotic device pre-learns environmental characteristics and task patterns, which are then reused when facing similar situations, reducing the need for adaptive reprogramming.
2Extent of automation
If robotic devices are remotely controlled by humans, then they can perform complex tasks, but human operator intervention is required continuously
Solution Approach 1:
The robotic device uses discrepancy detection as a feedback mechanism to monitor the difference between predicted and actual outcomes of its actions. This feedback drives autonomous learning by identifying when the device's internal models diverge from reality, triggering retraining episodes that improve prediction accuracy and enable more autonomous operation without continuous human supervision.
Solution Approach 2:
The system replaces manual control mechanisms with autonomous learning mechanisms. Instead of relying on human operators to control each action, the robotic device uses neural networks trained through discrepancy detection to autonomously determine appropriate actions, substituting human mechanical control with intelligent autonomous decision-making.
3Manufacturing precision
If programming is used to control robotic devices, then they can perform precise tasks, but costly programming and reprogramming are required
Solution Approach 1:
The robotic device employs dynamic learning where the neural network parameters are continuously updated based on environmental feedback and discrepancy detection. This dynamic adaptation allows the system to maintain precise task execution while automatically adjusting to new conditions, eliminating the need for costly manual reprogramming and improving cost efficiency.
Data Source
AI summary
A robotic device may comprise an adaptive controller configured to learn to predict consequences of robotic device's actions. During training, the controller may receive a copy of the planned and/or executed motor command and sensory information obtained based on the robot's response to the command. The controller may predict sensory outcome based on the command and one or more prior sensory inputs. The predicted sensory outcome may be compared to the actual outcome. Based on a determination that the prediction matches the actual outcome, the training may stop. Upon detecting a discrepancy between the prediction and the actual outcome, the controller may provide a continuation signal configured to indicate that additional training may be utilized. In some classification implementations, the discrepancy signal may be used to indicate occurrence of novel (not yet learned) objects in the sensory input and/or indicate continuation of training to recognize said objects.