Robot Action Correction Feedback for Adaptive Model Training
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Solution Overview
Problem
Robots often perform actions incorrectly due to inaccurate models and dynamic environments, and may not recognize these errors, leading to incorrect parameter determination and subsequent actions.
Innovation Solution
A method to generate correction instances using human input, which include sensor data and incorrect parameter information, to train and update neural network models, allowing for improved action performance and model adaptation across multiple robots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robot models are used to determine action parameters, then automation is improved, but measurement precision deteriorates due to model inaccuracies
Solution Approach 1:
The system implements feedback by detecting discrepancies between robot actions and actual outcomes, then using this feedback to iteratively refine the models. Correction instances capture these discrepancies and are used to retrain models, creating a closed-loop system that continuously improves accuracy while maintaining automation.
Solution Approach 2:
The robot system performs self-improvement by automatically detecting its own performance errors through correction instances and using these to retrain its models. This self-service mechanism allows the system to autonomously enhance its measurement precision without external intervention for each correction.
2Loss of time
If models are trained on limited data, then training time is reduced, but reliability deteriorates due to insufficient training coverage
Solution Approach 1:
The system performs preliminary action by continuously collecting and storing correction instances during robot operation. These correction instances are accumulated over time and used to periodically retrain models, ensuring that training data is prepared in advance and models are updated with comprehensive real-world examples before deployment.
Solution Approach 2:
The training process is made dynamic by continuously updating models with new correction instances as they are collected. Rather than static periodic retraining, the system adapts its training data dynamically based on actual performance discrepancies encountered in varying environments, improving reliability without requiring excessive training time.
3Manufacturing precision
If robot models are highly specific to particular environments, then manufacturing precision is improved, but adaptability deteriorates when operating conditions change
Solution Approach 1:
The system achieves universality by training models on diverse correction instances from multiple robots operating in different environments. The models become multi-functional, capable of performing accurately across various settings rather than being specialized for a single environment, thus improving adaptability while maintaining precision through comprehensive training data.
Solution Approach 2:
The system adapts to environmental changes by dynamically adjusting model parameters based on correction instances collected from varying operating conditions. When environments change, the models receive new training data that reflects these parameter changes, allowing them to maintain high action accuracy across different settings without requiring complete retraining from scratch.
4Reliability
If correction instances are collected from multiple robots, then model robustness is improved, but device complexity increases due to data aggregation requirements
Solution Approach 1:
The system merges correction instances from multiple robots into a unified training dataset. By combining data from multiple sources, the models become more robust and generalize better. The merging process consolidates diverse correction instances while maintaining the essential information needed for effective model retraining, improving reliability without proportionally increasing complexity.
Solution Approach 2:
The system uses copying by creating standardized correction instance formats that can be replicated across multiple robots. This standardization allows for efficient data aggregation from multiple sources without requiring complex custom processing for each robot, reducing the complexity overhead while still benefiting from multi-robot data collection for improved model robustness.
Data Source
AI summary
Methods, apparatus, and computer-readable media for determining and utilizing human corrections to robot actions. In some implementations, in response to determining a human correction of a robot action, a correction instance is generated that includes sensor data, captured by one or more sensors of the robot, that is relevant to the corrected action. The correction instance can further include determined incorrect parameter(s) utilized in performing the robot action and/or correction information that is based on the human correction. The correction instance can be utilized to generate training example(s) for training one or model(s), such as neural network model(s), corresponding to those used in determining the incorrect parameter(s).