Robot Action Correction Learning for Dynamic Parameter Errors
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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 robots to adapt and improve their performance based on human corrections, even across disparate locations and environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robots use current models to determine parameters for actions, then they can perform actions autonomously, but the accuracy of parameter determination deteriorates due to model inaccuracies and dynamic environments
Solution Approach 1:
The system implements feedback by collecting human corrections when robots perform actions incorrectly. These corrections are transmitted to remote computing devices that generate revised models. The revised models are then transmitted back to robots, creating a closed-loop feedback system that continuously improves parameter determination accuracy while maintaining autonomous operation.
Solution Approach 2:
The system performs preliminary actions by generating revised models in advance based on accumulated human corrections from multiple robots. These pre-revised models are stored and ready for deployment, allowing robots to update their parameter determination capabilities without waiting for real-time corrections during operation.
2Adaptability or versatility
If robots operate in varied and dynamic environments, then their adaptability improves, but model accuracy deteriorates leading to incorrect action performance
Solution Approach 1:
The system creates universal revised models that can be applied across multiple robots operating in different environments. The correction instances collected from various robots in disparate geographic locations and environments are aggregated to generate models that work universally, allowing each robot to benefit from corrections observed in other environments while maintaining reliability.
Solution Approach 2:
The system merges correction instances from multiple robots into unified training datasets. By combining data from robots in different environments, the system creates comprehensive revised models that account for environmental variability, improving both adaptability and reliability simultaneously.
3Adaptability or versatility
If robots collect correction instances from multiple robots across disparate locations, then the training data diversity improves, but the system complexity increases
Solution Approach 1:
The system introduces remote computing devices as intermediaries between robots and the model training process. These intermediaries receive correction instances from multiple robots, generate revised models, and transmit them back to robots. This intermediary layer simplifies the complexity by centralizing the data aggregation and model generation tasks, allowing individual robots to remain relatively simple while benefiting from diverse training data.
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). In various implementations, the training is based on correction instances from multiple robots. After a revised version of a model is generated, the revised version can thereafter be utilized by one or more of the multiple robots.