Robotic Manipulation Model Adaptation from Simulation to Real Grasping
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Solution Overview
Problem
Existing machine learning-based approaches for robotic grasping require extensive real-world physical robot usage, leading to time-consuming, resource-intensive, and wear-and-tear-prone training processes that necessitate significant human intervention.
Innovation Solution
A method involving a machine learning model trained using simulated data from simulated robots manipulating simulated objects, with subsequent adaptation using real-world data to improve performance on real-world tasks, reducing the reliance on real-world training examples and enhancing efficiency and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training is performed using real-world physical robot data, then the model learns accurate real-world manipulation patterns, but the training process consumes excessive time, resources, and causes wear and tear on physical robots
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model using simulated robot manipulation data before deploying it to real physical robots. This allows the model to learn fundamental manipulation patterns in advance from synthetic data, so that when it is later fine-tuned on real-world data, the training process is significantly accelerated and requires fewer real-world examples.
Solution Approach 2:
The patent uses copying by creating simulated versions of robots and manipulation scenarios that replicate real-world physics and interactions. These simulated copies generate training data that mimics real-world conditions without requiring actual physical robots, thereby reducing the time and wear associated with collecting real-world training data while maintaining model accuracy.
2Reliability
If extensive real-world robot usage is used for training, then the model achieves better performance, but resource consumption and wear and tear on physical robots increase significantly
Solution Approach 1:
The patent creates virtual copies of physical robots and their operating environments through simulation. These simulated robots perform the bulk of training iterations, generating large datasets without consuming physical resources. Only minimal real-world robot usage is needed for final fine-tuning, dramatically improving resource utilization efficiency while maintaining model performance.
Solution Approach 2:
The patent performs preliminary training in simulation before actual deployment. This pre-training phase allows the model to learn from thousands of simulated examples without using physical robots, so that when real robots are used for fine-tuning, the resource consumption is minimized while still achieving high performance.
3Adaptability or versatility
If large quantities of real training examples are collected from physical robots, then the model generalizes better to real-world tasks, but the process requires significant human intervention and is time-consuming
Solution Approach 1:
The patent uses simulated copies to generate training data that automatically captures diverse manipulation scenarios without human intervention. The simulation environment can autonomously generate varied objects, positions, and manipulation sequences, eliminating the need for humans to manually set up each training example while still providing realistic data for better generalization.
Solution Approach 2:
The patent performs preliminary data generation and model pre-training in simulation before real-world deployment. This automated simulation process generates large quantities of training examples without human intervention, and the pre-trained model requires minimal human-assisted fine-tuning, thereby improving adaptability while reducing the extent of automation needed during data collection.
Data Source
AI summary
Implementations are directed to training a machine learning model that, once trained, is used in performance of robotic grasping and/or other manipulation task(s) by a robot. The model can be trained using simulated training examples that are based on simulated data that is based on simulated robot(s) attempting simulated manipulations of various simulated objects. Portion(s) of the model can also be trained based on real training examples that are based on data from real-world physical robots attempting manipulations of various objects. The simulated training examples can be utilized to train the model to predict an output that can be utilized in a particular task—and the real training examples used to adapt at least a portion of the model to the real-world domain can be tailored to a distinct task. In some implementations, domain-adversarial similarity losses are determined during training, and utilized to regularize at least portion(s) of the model.