Robotic Grasp Action Prediction for Visual Servoing Accuracy
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Solution Overview
Problem
Current robotic systems face challenges in determining successful grasp actions for objects in complex environments, as existing methods lack efficiency and accuracy in predicting successful robot grasp poses, leading to low success rates and increased training iterations.
Innovation Solution
The use of neural network models, specifically action prediction networks, which incorporate Gaussian mixture models, real-valued non-volume preserving transformations, and hybrid models, to generate predicted probability distributions of successful grasp actions by processing input images and identifying optimal end effector movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional robotic grasping systems are used, then the system structure is simple, but the grasp success rate is low and training iterations are increased
Solution Approach 1:
The system segments the grasping task into multiple components: object detection, pose estimation, and grasp action prediction. The neural network model is divided into distinct modules including convolutional layers for feature extraction and fully connected layers for action prediction, allowing each segment to be optimized independently while improving overall grasp success rate
Solution Approach 2:
The system performs preliminary actions by pre-training the neural network model with synthetic data before deployment. The model undergoes preliminary training iterations using rendered images and simulated grasp outcomes, establishing a strong foundation that reduces the need for extensive real-world training iterations and improves initial grasp success rate
2Measurement precision
If more training iterations are performed, then the network accuracy improves, but the training time is increased
Solution Approach 1:
The system performs preliminary training using large sets of synthetic training data generated through rendering, allowing the network to learn fundamental grasp patterns before deployment. This preliminary action with synthetic data significantly reduces the number of real-world training iterations needed, achieving high prediction accuracy faster
Solution Approach 2:
The system creates synthetic copies of real-world scenarios through rendering and simulation, generating virtual training data that replicates physical grasp outcomes. These copied training examples allow the network to learn from diverse grasp situations without requiring extensive physical experimentation, reducing training time while maintaining prediction accuracy
Data Source
AI summary
Deep machine learning methods and apparatus related to the manipulation of an object by an end effector of a robot are described herein. Some implementations relate to training an action prediction network to predict a probability density which can include candidate actions of successful grasps by the end effector given an input image. Some implementations are directed to utilization of an action prediction network to visually servo a grasping end effector of a robot to achieve a successful grasp of an object by the grasping end effector.