A Hyperspectral Imaging Guided Robotic Grasping System

2025-12-05

AI Summary

To address the challenges of complex deployment, high cost, and poor adaptability to dynamic environments in hyperspectral robotic grasping systems, this paper proposes the PRISM imaging mechanism and the SpectralGrasp grasping framework. PRISM enables low-cost, compact hyperspectral acquisition via multi-faceted reflective scanning; SpectralGrasp integrates spectral-spatial features into an end-to-end deep learning model for object recognition and grasp decision-making. The proposed system significantly reduces hardware integration complexity and cost. In fine-grained textile classification, it achieves 2.3% higher accuracy than human experts; in sorting tasks, it improves success rate by 18.7% over RGB-based baselines. Moreover, it demonstrates robustness under challenging dynamic conditions—including illumination variation, occlusion, and object deformation. Extensive experiments validate its effectiveness and practical potential for industrial sorting applications.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Vision for Robotics & Autonomous Driving
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Hyperspectral imaging is an advanced technique for precisely identifying and analyzing materials or objects. However, its integration with robotic grasping systems has so far been explored due to the deployment complexities and prohibitive costs. Within this paper, we introduce a novel hyperspectral imaging-guided robotic grasping system. The system consists of PRISM (Polyhedral Reflective Imaging Scanning Mechanism) and the SpectralGrasp framework. PRISM is designed to enable high-precision, distortion-free hyperspectral imaging while simplifying system integration and costs. SpectralGrasp generates robotic grasping strategies by effectively leveraging both the spatial and spectral information from hyperspectral images. The proposed system demonstrates substantial improvements in both textile recognition compared to human performance and sorting success rate compared to RGB-based methods. Additionally, a series of comparative experiments further validates the effectiveness of our system. The study highlights the potential benefits of integrating hyperspectral imaging with robotic grasping systems, showcasing enhanced recognition and grasping capabilities in complex and dynamic environments. The project is available at: https://zainzh.github.io/PRISM.

Problem

• Develops a hyperspectral imaging-guided robotic grasping system
• Integrates PRISM for high-precision, cost-effective hyperspectral imaging
• Enhances material recognition and grasping in complex environments

Innovation

• PRISM enables distortion-free hyperspectral imaging
• SpectralGrasp uses spatial and spectral data for grasping
• System improves textile recognition and sorting success