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 data collection, which is time-consuming, resource-intensive, and causes wear and tear on physical robots.
Innovation Solution
The implementation of a machine learning method that trains a model using simulated data for initial training, followed by adaptation with real-world data to improve performance in real-world robotic grasping tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained using only real-world physical robot data, then the model achieves high reliability for robotic grasping, but the training process becomes extremely time-consuming and resource-intensive
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model on simulated robot data before fine-tuning with real-world data. This allows the model to learn basic grasping patterns in advance from synthetic training examples, reducing the amount of time-consuming real-world data collection and training needed later while maintaining model reliability.
2Reliability
If extensive real-world robotic grasping attempts are conducted for data collection, then training data quality improves, but resource consumption and wear and tear on physical robots increase
Solution Approach 1:
The patent uses copying by creating simulated training examples that replicate real-world robotic grasping scenarios without requiring physical robot execution. The simulation environment generates synthetic training data that mirrors real-world conditions, allowing model training with high-quality data while avoiding the resource consumption and wear associated with physical robot operations.
3Measurement precision
If large quantities of real-world training data are collected through physical robot attempts, then model accuracy improves, but human intervention requirements increase
Solution Approach 1:
The patent applies self-service by enabling the simulation environment to automatically generate training examples without human intervention. The simulated robot autonomously performs grasping attempts in the virtual environment, automatically collecting and generating training data with proper labels and annotations, thereby improving model accuracy while eliminating the need for human operators to physically set up and monitor each training trial.
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.