Robot Action Correction Instances for Cross-Robot Model Adaptation
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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 collective corrections from multiple robots across different environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robots use current models to determine action parameters, then they can perform actions autonomously, but the actions may be incorrect due to model inaccuracies and dynamic environments
Solution Approach 1:
The system implements feedback by collecting human corrections when robots perform incorrect actions. These corrections are fed back into the training process to update and improve the models, creating a closed-loop system that continuously learns from errors and improves action correctness while maintaining autonomous operation
Solution Approach 2:
The system enables self-service by allowing robots to autonomously generate correction instances from their own incorrect actions and use these corrections to retrain their models. This self-learning mechanism reduces the need for external intervention while improving reliability over time
2Adaptability or versatility
If robots collect and process corrections from multiple robots across different environments, then model accuracy and adaptability improve, but system complexity increases
Solution Approach 1:
The system applies universality by creating a centralized training system that processes correction instances from multiple robots across different environments using a unified model architecture. This multi-functional system handles diverse corrections from various robot types and environments, improving adaptability while managing complexity through standardized processing pipelines
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.