Physics-Based Robot Training via Neural Network Inference
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Solution Overview
Problem
Current simulation frameworks for robotic applications are inadequate for testing mechanical interactions and real-time sensor data, as they either require high computational resources or simplify geometric models, limiting their ability to simulate complex robotic tasks effectively.
Innovation Solution
A physics-based simulated environment with a GPU-based collision detector and mechanical simulation framework, combined with a Hardware-in-the-Loop (HWIL) approach, allows for real-time simulation of complex robotic tasks and optimization of robot control programs using a virtual runtime environment that generates realistic sensor data and computes mechanical behavior based on Newtonian physics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If highly detailed geometric models and computational models are used to simulate realistic mechanical behavior, then simulation fidelity is improved, but runtime requirements increase significantly
Solution Approach 1:
The system segments the simulation pipeline into distinct phases: a physics-based simulation phase for generating training data with high fidelity models, and a real-time inference phase using pre-trained neural networks for rapid execution. This segmentation allows detailed mechanical models to be used during offline training without compromising real-time performance during actual robot operation.
Solution Approach 2:
The system performs preliminary action by pre-training neural network models using data from physics-based simulations with detailed geometric models. This offline preparation phase computes and stores the results of complex mechanical interactions in advance, enabling the robot to perform real-time inference using simple neural network lookups rather than re-computing complex physics in real-time.
2Productivity
If simplified geometric models are used to meet real-time constraints, then computation time is reduced, but simulation accuracy deteriorates
Solution Approach 1:
The system introduces neural networks as an intermediary between the detailed geometric models and real-time simulation requirements. The neural networks are trained offline using high-fidelity physics simulations and then serve as lightweight proxies that replicate the behavior of complex models in real-time without requiring the computational resources of the original detailed models.
3Speed
If basic computational models for classical mechanics are used, then computation speed is improved, but mechanical interaction realism deteriorates
Solution Approach 1:
The system creates copies of the complex mechanical interaction data generated by physics-based simulations and stores them in neural network weight parameters. Instead of re-computing complex mechanical interactions during real-time operation, the system copies the pre-computed results into the neural network model, allowing rapid inference while preserving the realism of detailed mechanical simulations.
4Measurement precision
If detailed geometric models of robots, tools, and work pieces are used for contact detection, then collision detection accuracy is improved, but processing time increases
Solution Approach 1:
The system replaces the traditional mechanical collision detection approach with a neural network-based system. Instead of performing real-time geometric computations to detect contacts between detailed models, the system uses neural networks that have been trained to predict contact outcomes, substituting complex mechanical calculations with efficient neural network inference.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient testing and optimization of robot control programs in a virtual environment, reducing the risk of downtimes and defects, and improving the reliability of robotic systems by simulating complex robotic tasks with high fidelity and real-time performance.
Implementation Method 1
GPU-based collision detector that computes contacts between complex 3D geometries in real-time
Implementation Method 2
computes mechanical behavior based on Newtonian physics
Data Source
AI summary
A disclosed system includes a physically plausible virtual runtime environment to simulate a real-life environment for the simulated robot and a test planning and testing component operable to receive task data based used to create a plurality of plausible tests for at least one robot control program. The plurality of plausible tests is designed to execute at least one task associated with the task data. The test planning and testing component is further operable to define test parameters for each of the plurality of plausible tests. The system further includes a robot controller operable to execute the plurality of plausible tests substantially simultaneously on the simulated robot, analyze results of the execution to select an optimized robot control program from the at least one robot control program, and based on the analysis, selectively optimize the test associated with the optimized robot control program.