Pose Estimation Uncertainty for Robotic Grasp Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Robotic systems face challenges in determining the pose of objects for grasping in scenarios where complete 3D models are not available, especially with noisy camera data, leading to limitations in grasp determination and manipulation tasks.
Innovation Solution
The use of two neural networks trained on different data sources to generate and compare poses of an object from multiple viewpoints, determining the uncertainty quantification between poses to select the most accurate grasp position, allowing robots to grasp objects even in cluttered environments without complete 3D information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete 3D models are used for pose generation, then grasp determination accuracy is improved, but system complexity and data requirements increase
Solution Approach 1:
The patent uses multiple neural networks as virtual copies to generate different pose estimates. Instead of requiring a single complete 3D model, the system creates multiple synthetic pose copies through different neural network predictions, then selects the most reliable one based on uncertainty metrics.
Solution Approach 2:
The system changes the parameter of pose estimation by using uncertainty quantification metrics to select between different pose estimates. Rather than relying on a single fixed 3D model, the system dynamically selects poses based on confidence levels derived from multiple neural network predictions.
2Reliability
If multiple neural networks are used to quantify uncertainty, then pose estimation reliability is improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by using a subset of multiple neural network predictions. Instead of processing all possible pose estimates equally, the system selectively processes and compares predictions from multiple networks only when needed to resolve uncertainty, reducing overall computational burden while maintaining reliability.
Solution Approach 2:
The system implements feedback through uncertainty quantification metrics that evaluate the agreement between multiple neural network predictions. This feedback mechanism allows the system to adaptively determine when multiple networks are needed versus when a single prediction suffices, optimizing computational resource usage.
Data Source
AI summary
Apparatuses, systems, and techniques generate poses of an object based on image data of the object obtained from a first viewpoint of the object and a second viewpoint of the object. The poses can be evaluated to determine a portion of the image data usable by an estimator to generate a pose of the object.