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A Novel Pipeline for Object Recognition Utilising Multi-Sensory Tactile Fusion
IEEE Transactions on Haptics
|February 3, 2026
Summary
This study introduces a novel tactile sensing approach for object recognition, outperforming visual systems in challenging conditions. Multi-sensory fusion with MiniROCKET significantly boosts accuracy and efficiency.
Area of Science:
- Robotics
- Artificial Intelligence
- Sensor Fusion
Background:
- Visual object recognition faces limitations due to illumination and occlusion.
- Tactile sensing offers complementary data for robust object identification.
- Existing systems struggle to fully integrate multi-modal tactile information.
Purpose of the Study:
- To develop a novel object recognition pipeline using multi-sensory tactile fusion.
- To enhance object recognition capabilities beyond traditional visual methods.
- To leverage the MiniROCKET time-series classifier for advanced tactile data processing.
Main Methods:
- A multi-sensory tactile fusion model was developed.
- The MiniROCKET time-series classifier was employed.
- Classification heads were implemented on ROCKET and MiniROCKET pipelines for multi-modality integration.
Main Results:
- The proposed system achieved state-of-the-art performance on the PHAC-2 dataset.
- Accuracy was improved by 3.3% compared to previous benchmarks.
- Computational costs were reduced by up to 90%.
Conclusions:
- Multi-sensory tactile fusion significantly enhances object recognition accuracy and efficiency.
- This approach surpasses combined visual-tactile systems in performance.
- The MiniROCKET-based pipeline offers a powerful solution for robust tactile object recognition.
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