Robot End Effector Visual Servoing Across Changing Viewpoints
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Solution Overview
Problem
Existing machine learning-based approaches for robotic grasping and manipulation are often inaccurate and resource-intensive, particularly when dealing with variations in viewpoint and require extensive real-world data and physical robot usage, leading to inefficiencies and wear and tear.
Innovation Solution
A recurrent neural network model is employed for visual servoing, utilizing simulated data to train the model across various viewpoints and environments, and then adapted with real-world data to improve robustness and accuracy, enabling efficient and flexible robotic manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained using real-world physical robot data, then the model can learn from actual robotic grasping scenarios, but it requires heavy usage of physical robots which is time-consuming, resource-intensive, and causes wear and tear
Solution Approach 1:
The patent creates virtual copies of physical robots and their environments through simulated training data. Instead of training models exclusively on real-world robot data, the system generates synthetic training examples that replicate physical robotic grasping scenarios, thereby reducing the need for extensive physical robot usage while maintaining training effectiveness
Solution Approach 2:
The patent performs preliminary data generation by creating simulated training data in advance. Virtual training environments and scenarios are prepared beforehand through simulation, allowing the model to be pre-trained on synthetic data before deployment, which reduces the time and resources needed for actual physical robot training
2Measurement precision
If machine learning models are trained on data from the same or similar viewpoint, then the model can be adapted for use in robots capturing images from the same viewpoint, but it becomes inaccurate and fails for robots capturing images from different viewpoints
Solution Approach 1:
The patent trains the machine learning model on simulated training data that explicitly includes multiple different viewpoints and camera positions. This universal training approach enables the model to function accurately across various robotic platforms and viewpoint configurations, rather than being specialized for a single viewpoint
Solution Approach 2:
The patent varies key parameters during training, including camera positions, viewing angles, and scene configurations in the simulated data. By changing these parameters across diverse training examples, the model learns to generalize across different viewpoints and maintains accuracy when deployed on robots with different camera configurations
3Reliability
If extensive real-world data collection is performed for training, then the model can learn from actual robotic scenarios, but it consumes large amounts of resources and requires a great deal of human intervention
Solution Approach 1:
The patent replaces resource-intensive real-world data collection with computationally efficient simulated data generation. Virtual environments replicate physical robotic scenarios without requiring actual physical robots, thereby significantly reducing energy consumption and resource usage while maintaining model training effectiveness
Solution Approach 2:
The system performs self-service by automatically generating its own training data through simulation. The trained model can then be fine-tuned on small amounts of real-world data if needed, reducing the need for extensive manual data collection and human intervention in the training process
Data Source
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AI summary
Training and/or using a recurrent neural network model for visual servoing of an end effector of a robot. In visual servoing, the model can be utilized to generate, at each of a plurality of time steps, an action prediction that represents a prediction of how the end effector should be moved to cause the end effector to move toward a target object. The model can be viewpoint invariant in that it can be utilized across a variety of robots having vision components at a variety of viewpoints and/or can be utilized for a single robot even when a viewpoint, of a vision component of the robot, is drastically altered. Moreover, the model can be trained based on a large quantity of simulated data that is based on simulator(s) performing simulated episode(s) in view of the model. One or more portions of the model can be further trained based on a relatively smaller quantity of real training data.