Stable Plane Estimation via Mesh Simulation for Object Placement
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Solution Overview
Problem
Existing methods for robotically placing objects in stable postures lack the ability to accurately estimate and verify stable planes for various objects, especially those not previously trained, without human intervention.
Innovation Solution
A method and system for estimating stable planes by simulating the interaction of a mesh with a flat plate model in virtual space, using artificial neural networks trained with large-scale training data to identify stable planes for objects, and verifying these planes under various conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to control robot arm posture for object placement, then the robot can place objects in predetermined postures, but the system lacks the ability to accurately estimate stable planes for various objects especially those not previously trained
Solution Approach 1:
The system performs preliminary actions by generating a large-scale training dataset through virtual simulations before actual deployment. The training data is pre-generated by simulating objects dropping onto surfaces from various angles and positions, creating comprehensive stable plane data for diverse object types. This preliminary data preparation enables the neural network to accurately estimate stable planes for unseen objects without requiring real-world trial and error.
Solution Approach 2:
The system creates virtual copies of physical objects and environments to generate training data. By rendering three-dimensional models of objects and surfaces in a virtual environment, the system can simulate countless placement scenarios without physical prototypes. These virtual copies allow the neural network to learn from synthesized data, improving its ability to generalize to real-world objects it has never encountered.
2Extent of automation
If manual intervention is used to determine stable planes for object placement, then accuracy can be maintained, but the process requires human intervention and is not fully automated
Solution Approach 1:
The system implements self-service by enabling the neural network to autonomously estimate stable planes for objects without human intervention. The trained model independently processes new object data, predicts stable placement planes, and guides robot execution. This self-service capability maintains high accuracy while achieving full automation, as the system learns to make reliable judgments through its training on comprehensive virtual data.
Solution Approach 2:
The system incorporates feedback mechanisms where the neural network's predictions are continuously refined through training on generated data. The virtual simulation process provides feedback by comparing predicted stable planes with actual stable planes observed in simulations, allowing the model to learn from errors and improve its estimation accuracy over time.
3Quantity of substance
If virtual simulation is used to generate training data by dropping meshes on flat plate models, then large-scale training data can be generated for neural networks, but the system complexity increases
Solution Approach 1:
The system replaces complex physical experimentation with computational simulation. Instead of physically manipulating objects and measuring stable planes in the real world, the system uses virtual reality environments and neural networks to model and predict stable planes. This substitution allows generation of large-scale training data without the complexity of physical test setups, as virtual simulations can be rapidly executed and modified through software.
Data Source
AI summary
A stable plane estimation method including acquiring a mesh for a target object, disposing a flat plate model, disposing the mesh above the flat plate model, and dropping the mesh disposed above the flat plate model toward the flat plate model, and sampling, as a stable plane an area in contact with the flat plate model among a plurality of points belonging to the mesh when the mesh dropped toward the flat plate model stops, to generate training data for an object controlled to be placed on a specific support surface.