Unified Goal Representation for Robust Robot Navigation Tasks
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Solution Overview
Problem
Existing machine learning models used in applications like autonomous driving and medical imaging lack robustness and may produce unforeseen errors, leading to safety issues and misdiagnoses.
Innovation Solution
A computer-implemented method utilizing a system of one or more computers configured to perform operations by virtue of software, firmware, hardware, or a combination of them. The method includes receiving commands, accessing representation spaces, updating these spaces with datasets, and using machine learning models to generate goal representations and update navigation steps based on sensor data and performance annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing machine learning models are used for navigation tasks, then the system can perform basic navigation functions, but the models lack robustness and may produce unforeseen errors leading to safety issues
Solution Approach 1:
The patent creates a unified goal representation that serves multiple navigation tasks simultaneously. By learning a shared representation space that captures commonalities across different navigation tasks (indoor/outdoor, following/wandering, short/long distance), the system achieves better generalization and robustness without task-specific models, thereby improving reliability while maintaining versatility
Solution Approach 2:
The system performs preliminary learning of a unified goal representation during a training phase before actual navigation tasks are executed. This pre-trained representation captures essential navigation patterns and relationships, enabling the model to generalize better to unseen tasks and scenarios, thus improving reliability in real-world deployment
2Adaptability or versatility
If multiple task-specific machine learning models are trained for different navigation scenarios, then the system can handle diverse navigation tasks, but the complexity of training and deploying multiple models increases
Solution Approach 1:
The patent merges multiple task-specific models into a single unified model that learns a shared goal representation. Instead of training separate models for indoor/outdoor navigation, following/wandering modes, and short/long distance tasks, the system combines all these tasks into one training framework that learns common patterns, significantly reducing model complexity while maintaining comprehensive task coverage
Solution Approach 2:
A single machine learning model is designed to handle multiple navigation tasks through unified goal representation. The model universally processes different task types (indoor/outdoor, following/wandering, short/long distance) using the same architecture and training approach, eliminating the need for multiple specialized models and simplifying the system
3Ease of manufacture
If traditional evaluation methods are used to assess machine learning models, then the evaluation process is simple, but unforeseen model mistakes may cause serious consequences in real-world applications
Solution Approach 1:
The system performs comprehensive model evaluation and validation during the training phase before deployment. By assessing model performance across diverse navigation scenarios and edge cases in advance, the system identifies and corrects potential safety issues before real-world deployment, ensuring higher reliability without complicating the evaluation process
Solution Approach 2:
The patent implements preliminary testing and validation protocols that prepare the system for potential failures and edge cases before actual deployment. By anticipating and addressing possible model mistakes in advance through rigorous evaluation, the system creates a safety buffer that prevents serious consequences in real-world applications
Data Source
AI summary
The systems and methods described herein may include one or more processors configured to receive a command from a user related to a subject; access a representation space associated with the command; receive a first dataset related to the command, a second dataset related to the subject, and a third dataset which includes subjects related to the command; update the representation space based on at least one of the first, second, and third dataset; generate a goal representation based on the representation space; receive, from a plurality of sensors, a sensor data of a current environment; generate a first and a second series of steps based on the goal representation and the current environment; annotate the sensor data based on performance of the first series of steps to generate an annotated senor data; and update the second series of steps based on the annotated sensor data.