Differentiable Virtual World Simulation for Real-World State Matching
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Solution Overview
Problem
Conventional physical simulators face challenges in achieving high accuracy simulations due to difficulties in accurately reproducing the real world using virtual models.
Innovation Solution
An information processing device with a differentiable physical simulator and neural networks that updates environment and control variables based on real-world observations to refine simulations and optimize robot control methods, enabling high accuracy simulations by iteratively adjusting input variables through backpropagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional forward calculation simulation is used, then the simulation device can perform basic physical simulation, but high accuracy simulation cannot be achieved
Solution Approach 1:
The patent implements feedback by comparing simulation results with actual robot execution results and using backpropagation to update environment variables and control parameters. The loss function calculates the difference between simulated and actual outcomes, and this error feedback drives iterative optimization of the virtual model parameters through gradient descent, enabling the simulation to progressively converge toward high accuracy.
Solution Approach 2:
The patent replaces traditional mechanical forward-calculation-based physical simulation with a neural network-based differentiable simulation system. Instead of relying on conventional physics engines alone, the system uses differentiable neural networks to model physical processes, allowing gradient-based optimization and enabling high-accuracy reproduction of real-world dynamics through learned parameters rather than purely analytical mechanics.
2Reliability
If virtual models are used to reproduce the real world, then simulation can be performed, but accurate reproduction of real world conditions is difficult
Solution Approach 1:
The patent dynamically adjusts environment variables and control parameters through iterative optimization. The system modifies parameters such as object masses, friction coefficients, and control gains based on gradient information from the loss function. By continuously changing these parameters to minimize the difference between simulation and reality, the virtual model progressively achieves accurate reproduction of real-world conditions.
Solution Approach 2:
The patent performs preliminary initialization of environment variables and control parameters before simulation execution. The system pre-sets plausible ranges and initial values for physical parameters based on prior knowledge or heuristics, which accelerates the subsequent optimization process and guides the neural network toward realistic solutions more efficiently.
3Adaptability or versatility
If simulation parameters are fixed, then simulation execution is simple, but adaptation to real-world changes is limited
Solution Approach 1:
The patent transforms the simulation system from static to dynamic by enabling real-time updates of environment variables and control parameters. The system continuously adapts parameters based on feedback from comparing simulation outcomes with actual robot execution results. This dynamic adjustment capability allows the simulation to track and adapt to real-world changes while maintaining computational efficiency through vectorized operations and optimized gradient calculations.
Data Source
AI summary
An information processing device includes at least one memory, and at least one processor configured to perform, based on a state of a virtual world and a predetermined environment variable, a simulation with respect to the state of the virtual world, the state of the virtual world being based on an observation result of a real world, and the simulation being differentiable, and update the predetermined environment variable so that a result of the simulation approaches a changed state of the virtual world, the changed state being based on an observation result of the real world that is observed after the real world has changed.