Robot Grasp Pose Prediction From Single-Image NeRF Representations
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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 employs a compact shape representation to directly retrieve grasping poses from an RGB-only viewpoint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional grasp proposal generation methods are used for complex objects, then accurate grasp predictions can be achieved, but significant computing resources (memory, time, processing power) are consumed
Solution Approach 1:
The system segments the grasp generation process into two distinct stages: (1) a fast coarse-grasp generation stage that produces multiple candidate grasps quickly, and (2) a refined grasp selection stage that evaluates and selects the best grasp from candidates. This segmentation allows the system to maintain accuracy while reducing overall computational burden by avoiding exhaustive processing of all possible grasps.
Solution Approach 2:
The system generates an excessive number of coarse grasp candidates initially, then applies partial processing only to the most promising candidates in the refinement stage. By using a threshold-based filtering mechanism, the system processes only a subset of candidates in detail, reducing computational resources while maintaining grasp prediction accuracy for the final selected grasp.
2Reliability
If detailed object analysis is performed to generate accurate grasp proposals, then grasp stability can be improved, but the time required for generation increases
Solution Approach 1:
The system performs preliminary action by generating multiple coarse grasp candidates quickly using simplified object analysis, then prepares for refined evaluation only of the most promising candidates. This preliminary coarse-grasp generation establishes a foundation of potential grasps without requiring detailed analysis, thereby reducing overall generation time while preserving the ability to achieve stable grasps in the refinement stage.
Solution Approach 2:
The system skips detailed analysis for most grasp candidates by using a fast coarse-grasp generation method that produces candidates without exhaustive object analysis. By rushing through the generation of multiple candidates with simplified processing and then selectively refining only the best candidates, the system reduces total generation time while maintaining grasp stability for the final selection.
3Adaptability or versatility
If comprehensive object features are processed to generate grasp proposals, then grasp accuracy for diverse objects can be achieved, but device complexity increases
Solution Approach 1:
The system achieves universality by designing a two-stage grasp generation framework that can handle diverse object types through a unified approach. The coarse-grasp generation stage uses object-agnostic features to produce candidates for any object type, while the refinement stage applies universal evaluation criteria. This multi-functional design allows the system to adapt to various objects without requiring object-specific processing pipelines, thereby managing complexity while maintaining versatility.
Solution Approach 2:
The system employs dynamics by adaptively adjusting the level of processing detail based on the object characteristics and candidate quality. For simple objects or early candidate generation, simplified processing is used; for complex objects or promising candidates, more detailed analysis is applied. This dynamic adjustment of processing intensity allows the system to achieve accurate grasp predictions for diverse objects while optimizing computational complexity for each specific case.
Data Source
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.