Graph Neural Grasp Prediction for 3D Deformable Objects

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Solution Overview

Problem

Predicting grasp outcomes for 3D deformable objects requires significant computing resources and existing methods are inefficient, particularly for complex objects.

Innovation Solution

A predictive graph neural network (GNN), referred to as DefGraspNets, is trained to simulate 3D stress and deformation fields using a finite element method-based grasp simulation, enabling fast gradient-based optimization for robotic grasping of deformable objects.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional simulation methods are used to predict grasp outcomes for 3D deformable objects, then prediction accuracy is maintained, but computing resources and time consumption increase significantly

Engineering Contradiction:

Improvegrasp outcome prediction accuracyVSAvoidprediction time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system pre-trains a graph neural network model offline using finite element method simulation data. This preliminary action creates a pre-computed knowledge base that can be quickly applied during runtime without requiring complex real-time simulations, thus reducing prediction time while maintaining accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent creates a neural network model that copies and learns from the complex finite element method simulation results. Instead of running expensive FEM simulations in real-time, the system uses a trained neural network that replicates FEM behavior, providing fast predictions with comparable accuracy

Inventive Principle:

Principle #26Copying

2Reliability

If complex objects are analyzed for grasp prediction, then prediction reliability improves, but computing resources required increase

Engineering Contradiction:

Improvegrasp prediction reliabilityVSAvoidcomputing resource consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent segments the complex object into a graph representation where vertices and edges capture geometric and physical properties. This segmentation allows the graph neural network to process complex objects efficiently by breaking them down into manageable components that can be analyzed in parallel

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent replaces the computationally intensive finite element method mechanical simulation system with a neural network-based predictive model. This substitution maintains prediction reliability for complex objects while dramatically reducing computing resource consumption during inference

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If real-time grasp planning is implemented, then productivity increases, but prediction accuracy may deteriorate

Engineering Contradiction:

Improvegrasp planning speedVSAvoidgrasp outcome prediction accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system performs preliminary training of the graph neural network offline using high-fidelity finite element method data. This pre-computation enables real-time inference while maintaining the accuracy characteristics of complex simulations, resolving the trade-off between speed and precision

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12420420B2Predicting grasp outcomes

Publication Date: 2025.09.23 NVIDIA CORP

AI summary

Apparatuses, systems, and techniques to generate a predicted outcome of an object resulting from a robotic component applying a force. In at least one embodiment, a predicted outcome of an object resulting from a robotic component applying a force is generated based on, for example, a neural network.