Robotic Grasp Prediction Using Geometry-Aware 3D Features
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Solution Overview
Problem
Current robotic systems face challenges in accurately predicting the success of grasping objects using end effectors, particularly due to limitations in representing and utilizing 3D geometry features for grasp pose determination.
Innovation Solution
The implementation of deep machine learning methods involving a geometry network and a grasp outcome prediction network, which utilize trained neural network models to generate geometry-aware representations from 2D or 2.5D images, and apply these representations to predict the likelihood of successful grasping. This includes training networks using user-guided grasp attempts in virtual reality environments and automatically generating additional training instances to improve robustness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used for grasp pose determination without geometry-aware representations, then device complexity is reduced, but grasp outcome prediction accuracy deteriorates
Solution Approach 1:
The system segments the grasp prediction task into two distinct neural network components: a geometry network that extracts 3D geometry features from 2D images, and a grasp outcome prediction network that uses these features to predict grasp success. This segmentation allows each network to specialize in specific functions, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The geometry-aware representation serves as an intermediary between the 2D image input and the grasp outcome prediction. This intermediate representation encodes 3D geometry features that bridge the gap between simple image data and complex grasp prediction, enabling accurate predictions without requiring the final prediction network to directly process raw images.
2Reliability
If extensive training data is collected manually through user-guided attempts, then model robustness is improved, but loss of time increases
Solution Approach 1:
The system creates virtual copies of training instances by rendering synthetic images from 3D models in a virtual environment. These synthesized training examples are generated automatically without requiring physical objects or manual data collection, yet provide diverse and sufficient training data for robust model training.
Solution Approach 2:
The system performs preliminary rendering of 3D objects from multiple viewpoints and grasp poses to pre-generate training images before actual training begins. This preliminary action creates a comprehensive training dataset in advance, eliminating the need for time-consuming manual data collection during the training process.
3Measurement precision
If high-resolution 3D geometry representations are generated for all objects, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The geometry network focuses computational resources on extracting geometry features from relevant regions of the image rather than processing entire high-resolution images uniformly. By concentrating computational effort on local geometry-aware representations around potential grasp points, the system achieves accurate geometry understanding with reduced overall computational energy consumption.
Data Source
AI summary
Deep machine learning methods and apparatus, some of which are related to determining a grasp outcome prediction for a candidate grasp pose of an end effector of a robot. Some implementations are directed to training and utilization of both a geometry network and a grasp outcome prediction network. The trained geometry network can be utilized to generate, based on two-dimensional or two-and-a-half-dimensional image(s), geometry output(s) that are: geometry-aware, and that represent (e.g., high-dimensionally) three-dimensional features captured by the image(s). In some implementations, the geometry output(s) include at least an encoding that is generated based on a trained encoding neural network trained to generate encodings that represent three-dimensional features (e.g., shape). The trained grasp outcome prediction network can be utilized to generate, based on applying the geometry output(s) and additional data as input(s) to the network, a grasp outcome prediction for a candidate grasp pose.