Multi-Robot Test Orchestration for Diverse Training Data
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Solution Overview
Problem
Current robotic systems lack an efficient method to operate multiple robots in diverse environments with varying hardware and software components, and environmental conditions, which limits their ability to generate robust and diverse data for training and performance analysis.
Innovation Solution
A system that receives robot instructions and environmental parameters, configures the operating space of each robot based on these parameters, and stores data generated during operation, providing additional data for analysis and training, such as sensor data and updated machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple robots with different hardware and software components operate in diverse environments, then data diversity and robustness improve, but system complexity and coordination difficulty increase
Solution Approach 1:
The system segments the robot fleet into multiple independent testing robots, each capable of autonomous operation with its own hardware and software configuration. This segmentation allows diverse data generation while maintaining individual robot simplicity, resolving the contradiction between data diversity and system complexity.
Solution Approach 2:
The system implements a universal data collection framework that can accommodate multiple robot types with different hardware and software components. The standardized interface and common data structure allow heterogeneous robots to contribute to a unified dataset, enabling adaptability without proportionally increasing system complexity.
2Reliability
If robots operate in varied environmental conditions with different objects and configurations, then operational robustness improves, but testing and configuration time increase
Solution Approach 1:
The system pre-configures multiple robots with different hardware and software components before deployment. Environmental parameters and object configurations are prepared in advance, allowing robots to immediately begin testing in diverse conditions without time-consuming setup during operation, thus improving operational robustness while reducing configuration time.
3Device complexity
If homogeneous robot hardware and software are used, then system management simplifies, but data diversity and training robustness decrease
Solution Approach 1:
The system applies local quality by allowing individual robots to have specialized hardware and software configurations tailored to specific testing scenarios, while maintaining standardized communication and data reporting protocols. This enables data diversity at the local robot level without compromising overall system manageability through standardized interfaces.
Data Source
AI summary
Methods and apparatus related to receiving a request that includes robot instructions and/or environmental parameters, operating each of a plurality of robots based on the robot instructions and/or in an environment configured based on the environmental parameters, and storing data generated by the robots during the operating. In some implementations, at least part of the stored data that is generated by the robots is provided in response to the request and/or additional data that is generated based on the stored data is provided in response to the request.