Simulation Data Generation for Robot Training
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Solution Overview
Problem
Conventional methods for training machine learning models to control robots are labor-intensive and time-consuming, often resulting in insufficient training data, which can lead to inadequate model performance in real-world environments due to limited exposure to diverse tasks and environments.
Innovation Solution
A computer-implemented method generates simulation data by creating multiple simulation environments and tasks for robots, computing and refining robot trajectories, and using physics simulators to generate training data, including rendered images, to train machine learning models effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world training is used to train machine learning models, then the training data reflects actual physical conditions, but the training process is labor-intensive and time-consuming
Solution Approach 1:
The patent creates virtual copies of real-world environments, robots, and objects in a simulation engine. These digital twins replicate physical conditions without requiring physical presence, enabling automated data generation that eliminates manual setup while preserving realistic training scenarios
Solution Approach 2:
The system pre-generates large volumes of training data through automated simulation runs before actual deployment. By performing preliminary data collection in virtual environments, the system eliminates the need for time-consuming real-world data collection while ensuring sufficient training data is available
2Reliability
If real-world training is used to collect training data, then the data represents authentic operational scenarios, but the amount of training data generated is insufficient
Solution Approach 1:
The simulation system dynamically generates diverse training scenarios by varying environmental parameters, object properties, and task configurations. This automated dynamic generation creates vast quantities of authentic training data covering edge cases and rare scenarios that would be impossible to collect manually in the real world
Solution Approach 2:
The simulation engine serves multiple functions simultaneously: it replicates physical environments, generates diverse task scenarios, controls robotic agents, collects sensory data, and annotates training data. This multi-functional approach efficiently produces large volumes of comprehensive training data from a single system
3Reliability
If real-world training is used with physical robots, then the training captures actual sensor data, but the process requires extensive hardware maintenance and operational complexity
Solution Approach 1:
The system creates accurate digital replicas of physical sensors, robots, and environments in the simulation. These virtual sensors generate data that mimics real sensor outputs including noise characteristics and measurement properties, eliminating the need for physical hardware while maintaining data authenticity
Solution Approach 2:
The simulation engine acts as an intermediary that bridges the gap between virtual training and real-world deployment. It provides a controlled environment where training data can be generated with realistic sensor characteristics without the complexity of physical hardware setup, maintenance, and operational constraints
Data Source
AI summary
One embodiment of a method for generating simulation data to train a machine learning model includes generating a plurality of simulation environments based on a user input, and for each simulation environment included in the plurality of simulation environments: generating a plurality of tasks for a robot to perform within the simulation environment, performing one or more operations to determine a plurality of robot trajectories for performing the plurality of tasks, and generating simulation data for training a machine learning model by performing one or more operations to simulate the robot moving within the simulation environment according to the plurality of trajectories.