NeRF-Based Grasp Pose Prediction From a Single Image

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Solution Overview

Problem

Generating grasp proposals for robots to grasp objects requires significant computing resources, particularly for complex objects, leading to inefficiencies in memory, time, and computing demands.

Innovation Solution

A system utilizing neural radiance fields (NeRFs) for object rendering and grasp pose prediction, which infers grasping poses from a single image, leveraging category-level priors and fine-tuning with minimal data and time, to generate grasp proposals efficiently.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional methods are used to generate grasp proposals for complex objects, then grasp accuracy can be maintained, but computing resources (memory, time, processing power) are excessively consumed

Engineering Contradiction:

Improvegrasp pose accuracyVSAvoidcomputing resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system segments the grasp generation process into two distinct stages: (1) a fast filtering stage that uses a lightweight neural network to quickly eliminate obviously poor grasp candidates, and (2) a detailed evaluation stage that applies more computationally intensive methods only to the reduced set of promising candidates. This segmentation reduces overall computing resource consumption while maintaining grasp accuracy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system applies full computational effort only partially - specifically, intensive grasp evaluation is performed only on a small subset of candidates that pass the initial filtering stage, rather than applying exhaustive analysis to all possible grasp poses. This partial application of computational resources significantly reduces energy consumption while maintaining accuracy for the most promising grasps.

Inventive Principle:

Principle #16Partial or excessive action

2Reliability

If comprehensive grasp evaluation is performed for all possible poses, then grasp reliability is improved, but processing time increases significantly

Engineering Contradiction:

Improvegrasp success rateVSAvoidgrasp proposal generation time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary filtering of grasp candidates using a fast, lightweight evaluation before committing to more time-consuming detailed assessments. This preliminary action identifies and eliminates poor candidates early in the process, ensuring that comprehensive evaluation is only applied to promising candidates, thus reducing total processing time while maintaining reliability.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements a continuous, multi-stage evaluation pipeline where candidates progress through increasingly detailed assessment only if they meet criteria at previous stages. This continuous filtering process ensures that useful computational action is maintained throughout the pipeline while avoiding wasteful processing of obviously poor candidates, balancing reliability and time efficiency.

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS12472628B2Grasp pose prediction

Publication Date: 2025.11.18 NVIDIA CORP

AI summary

Apparatuses, systems, and techniques to generate and select grasp proposals. In at least one embodiment, grasp proposals are generated and selected using one or more neural networks, based on, for example, a latent code corresponding to an object.