Injecting Noise into Robot Simulation for Realistic Training
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Solution Overview
Problem
Robot simulation lacks realism due to the absence of real-world stimuli, which can lead to insufficient training of machine learning models for robot behavior.
Innovation Solution
Injecting noise into joint commands and sensor data within the simulated environment to mimic real-world conditions, such as motor tolerances and sensor perturbations, thereby enhancing the realism of robot simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If robot simulation is used to generate training episodes, then training cost and time are reduced, but realism of the simulation deteriorates
Solution Approach 1:
The patent converts the harmful effect of simulation unrealisticness into a beneficial training tool by intentionally injecting noise into sensor data and joint commands. This noise, which normally degrades simulation quality, is systematically applied to bridge the gap between simulated and real-world conditions, enabling robots to learn robust policies that generalize better to physical environments.
Solution Approach 2:
The patent changes the parameters of the simulation by introducing controlled noise variations in sensor readings and joint commands. By modifying these parameters systematically with different noise levels and types, the simulation transitions from an idealized environment to one that better reflects real-world uncertainties, thereby improving training effectiveness without requiring extensive physical robot operation.
2Reliability
If noise is injected into joint commands and sensor data, then realism of simulation is improved, but complexity of the simulation system increases
Solution Approach 1:
The patent introduces noise injection modules as intermediary components between the robot controller and the simulation environment. These intermediaries systematically add noise to joint commands and sensor data without fundamentally restructuring the entire simulation system. The noise injection layer acts as a mediator that bridges the gap between idealized simulation and real-world conditions while maintaining the existing simulation architecture.
Solution Approach 2:
The patent segments the noise injection process into distinct components: one module for injecting noise into joint commands and another for injecting noise into sensor data. This segmentation allows each noise injection mechanism to be independently configured and managed, reducing the overall complexity by breaking down the problem into manageable, modular parts rather than implementing a monolithic complex system.
Data Source
AI summary
Implementations are provided for increasing realism of robot simulation by injecting noise into various aspects of the robot simulation. In various implementations, a three-dimensional (3D) environment may be simulated and may include a simulated robot controlled by an external robot controller. Joint command(s) issued by the robot controller and/or simulated sensor data passed to the robot controller may be intercepted. Noise may be injected into the joint command(s) to generate noisy commands. Additionally or alternatively, noise may be injected into the simulated sensor data to generate noisy sensor data. Joint(s) of the simulated robot may be operated in the simulated 3D environment based on the one or more noisy commands. Additionally or alternatively, the noisy sensor data may be provided to the robot controller to cause the robot controller to generate joint commands to control the simulated robot in the simulated 3D environment.