A Hyperspectral Imaging Guided Robotic Grasping System
Zheng SUN, Zhipeng Dong, Shixiong Wang, zhongyi chu, Fei Chen
AI summary
Problem
Hyperspectral imaging remains underutilized in close-range robotics due to high costs, complex mechanical requirements, and computational bottlenecks, while often neglecting valuable spatial context alongside spectral data.
Approach
The authors introduce PRISM, a compact rotational-scanning hyperspectral imager that eliminates conveyor mechanisms, and SpectralGrasp, a framework that fuses spectral and spatial image features to predict optimal robotic suction points and trajectories.
Key results
- PRISM delivers high-precision, distortion-free hyperspectral imaging at significantly reduced cost
- SpectralGrasp effectively extracts object-level classifications and grasp points from hyperspectral data
- The system achieves superior textile material recognition accuracy compared to human operators
- Sorting success rates exceed those of conventional RGB-based robotic grasping methods
Why it matters
This work provides a practical, cost-effective pathway for deploying hyperspectral perception in close-range robotic manipulation, advancing automated sorting and material handling in dynamic environments.
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 ex- plored 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.