Domain-Invariant 3D Representations for Simulated Robot Training
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Solution Overview
Problem
Existing machine learning-based robotic control approaches require extensive real-world data collection, which is time-consuming, resource-intensive, and causes wear to physical robots, while simulated data often lacks accuracy due to the 'reality gap' between simulated and real environments.
Innovation Solution
Training a point cloud prediction model to generate domain-invariant 3D representations from 2.5D observations, allowing for efficient training of robotic manipulation policies using primarily simulated data, mitigating the reality gap and enabling accurate robotic control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world physical robots are used to generate training data, then the training data accurately reflects real environment conditions, but the process is time-consuming, resource-intensive, and causes wear to physical robots
Solution Approach 1:
The patent uses robotic simulators to create virtual copies of physical robots and environments. These simulated robots generate training data that mimics real-world conditions without requiring actual physical robots to perform repetitive data collection tasks, thereby reducing time and wear while maintaining data accuracy through realistic simulation physics and rendering.
Solution Approach 2:
The patent pre-trains machine learning models using extensively generated simulated training data before deploying them to physical robots. This preliminary training in simulation allows the models to learn fundamental patterns and behaviors in advance, reducing the need for time-consuming real-world data collection and fine-tuning later.
2Productivity
If robotic simulators are used to generate simulated training data, then the training process is faster and less resource-intensive, but a 'reality gap' exists between simulated and real environments that reduces accuracy
Solution Approach 1:
The patent employs domain adaptation techniques that dynamically adjust simulation parameters to better match real-world conditions. By modifying physical parameters, material properties, and environmental conditions in the simulator, the training data becomes more representative of real scenarios, reducing the reality gap while maintaining high training efficiency.
Solution Approach 2:
The patent implements feedback loops where performance differences between simulated and real robot executions are analyzed and used to refine the simulation model. This continuous improvement process reduces the reality gap over time, allowing the system to maintain both high productivity in simulation and high reliability when deployed to physical robots.
3Reliability
If extensive real-world training data is collected to mitigate the reality gap, then the accuracy of robotic manipulation improves, but the time and resources required for data collection increase significantly
Solution Approach 1:
The patent uses a small amount of real-world training data strategically applied after extensive simulated training. Rather than requiring large quantities of real data, the system performs partial fine-tuning on real-world data to correct remaining domain differences, achieving high performance with minimal real-world data collection while maintaining fast training throughput.
Data Source
AI summary
Implementations relate to training a point cloud prediction model that can be utilized to process a single-view two-and-a-half-dimensional (2.5D) observation of an object, to generate a domain-invariant three-dimensional (3D) representation of the object. Implementations additionally or alternatively relate to utilizing the domain-invariant 3D representation to train a robotic manipulation policy model using, as at least part of the input to the robotic manipulation policy model during training, the domain-invariant 3D representations of simulated objects to be manipulated. Implementations additionally or alternatively relate to utilizing the trained robotic manipulation policy model in control of a robot based on output generated by processing generated domain-invariant 3D representations utilizing the robotic manipulation policy model.