Multi-Robot Test Environment Configuration 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 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 a single robot is operated in a standardized environment, then the system configuration is simple and easy to control, but the data diversity and robustness are limited
Solution Approach 1:
The system segments the robot fleet into multiple independent testing robots, each capable of operating in its own configured environment. This segmentation allows diverse data collection while maintaining individual robot simplicity, resolving the contradiction between data diversity and system complexity.
Solution Approach 2:
The system implements a universal robot platform that can be configured for multiple different environments and tasks. Each robot serves multiple functions through software configuration rather than hardware redesign, enabling diverse data collection without proportionally increasing physical system complexity.
2Reliability
If multiple robots with diverse hardware and software components are deployed, then data robustness and diversity improve, but system management and coordination become more complex
Solution Approach 1:
The system implements feedback mechanisms where robots report their operational status, environmental parameters, and data quality metrics to a central management system. This feedback enables automated monitoring and coordination, managing the complexity of diverse robot fleets while maintaining data robustness through continuous validation.
Solution Approach 2:
The system manages diversity by parameterizing robot configurations and environments rather than hardcoding each variant. This allows systematic control over hardware and software variations, making diverse robot fleets manageable through parameter adjustment rather than complex individual management.
3Adaptability or versatility
If robots operate in varied environmental conditions, then the operational capabilities and adaptability improve, but the difficulty of detecting and measuring environmental parameters increases
Solution Approach 1:
The system performs preliminary configuration of environmental parameters and robot capabilities before deployment. By pre-defining and validating environmental conditions and sensor capabilities, the system reduces the difficulty of detecting and measuring parameters during actual operation while maintaining adaptability to varied conditions.
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.