Multi-Robot Virtual Environment Synchronization for ML Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Training machine learning models for robots requires extensive real-world repetitions, which is time-consuming and costly. Additionally, different robots with varying capabilities and constraints operate in the same environment, necessitating separate training for each robot to interact effectively with dynamic aspects like other robots, humans, and objects.
Innovation Solution
The implementation involves operably coupling multiple robot controllers to a single simulated environment, allowing each robot avatar to be controlled independently and perform tasks within the virtual environment. This setup generates training examples for machine learning models, and a simulated world clock can be adjusted to account for frequency deviations and lag, ensuring deterministic reproducibility of robotic tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world physical robots are used to train machine learning models through repeated tasks, then the training data reflects actual physical interactions and dynamics, but the time and cost required for sufficient training becomes extremely large
Solution Approach 1:
The patent creates virtual copies (simulations) of physical robots, environments, and tasks. These digital twins replicate the physical system's behavior, dynamics, and interactions, allowing unlimited repetitions in virtual space. The simulation engine generates training data that mirrors real-world physics while eliminating the time and resource constraints of physical experimentation.
Solution Approach 2:
The system performs preliminary training actions in the virtual environment before deploying to physical robots. By conducting extensive repeated tasks in simulation first, the machine learning models achieve sufficient training beforehand, reducing the need for time-consuming real-world repetitions and enabling faster iteration and development.
2Adaptability or versatility
If separate training is conducted for each robot type to account for different capabilities and constraints, then each robot's machine learning model is optimized for its specific hardware, but the complexity and time required to train multiple robots increases
Solution Approach 1:
The simulation environment serves as a universal training platform that can accommodate multiple robot types with different capabilities, constraints, and hardware configurations. By configuring the virtual environment to match each robot's specific characteristics, the system enables standardized training procedures that work across diverse robot platforms without requiring separate physical training setups for each type.
Solution Approach 2:
The system customizes the simulation environment to reflect each robot's local characteristics, such as specific sensor capabilities, actuator constraints, and hardware limitations. This localized adaptation within the universal simulation framework allows each robot to receive tailored training that accounts for its unique properties while maintaining system-wide consistency and reusability.
3Productivity
If simulations are used to train machine learning models, then the time and cost of training are reduced, but ensuring that the simulation accurately reflects real-world physics and robot behavior becomes challenging
Solution Approach 1:
The simulation system creates faithful digital replicas of physical robots, environments, and task dynamics. By carefully modeling the physics engine, sensor behaviors, and actuator characteristics to match real-world systems, the simulation produces training data that accurately reflects physical reality, enabling high-fidelity training without sacrificing speed or efficiency.
4Productivity
If multiple robot controllers are coupled to a single simulated environment, then training data for multiple robots can be generated simultaneously, but managing frequency deviations and lag between controllers requires additional complexity
Solution Approach 1:
The simulation system employs periodic synchronization cycles and time-stepped updates to coordinate multiple robot controllers operating at different frequencies. By establishing regular update intervals and using deterministic time management, the system harmonizes controllers with varying operational rates, ensuring consistent state synchronization and deterministic reproducibility across the multi-robot training environment.
Data Source
AI summary
Implementations are provided for operably coupling multiple robot controllers to a single virtual environment, e.g., to generate training examples for training machine learning model(s). In various implementations, a virtual environment may be simulated that includes an interactive object and a plurality of robot avatars that are controlled independently and contemporaneously by a corresponding plurality of robot controllers that are external from the virtual environment. Sensor data generated from a perspective of each robot avatar of the plurality of robot avatars may be provided to a corresponding robot controller. Joint commands that cause actuation of one or more joints of each robot avatar may be received from the corresponding robot controller. Joint(s) of each robot avatar may be actuated pursuant to corresponding joint commands. The actuating may cause two or more of the robot avatars to act upon the interactive object in the virtual environment.