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Gang Ma

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3 papers

2 author rows

IROS Conference 2025 Conference Paper

Enhancing Tactile Sensing in Robotics Using Null-Space Diffusion Model with EIT-based Sensors

Qilin Zhang
Haofeng Chen
Xuanxuan Yang
Gang Ma
Xiaojie Wang

Robotic tactile sensors based on Electrical Impedance Tomography (EIT) have gained great attention in robotic sensing applications due to their features such as no internal wiring, "all-in-one" structure, and continuous sensing capabilities. However, the effectiveness of EIT-based tactile sensors is hampered by limited spatial resolution and artifacts in the reconstructed images. To address these challenges, various iterative optimization methods based on spatial regularizations and model-based methods have been proposed. In this study, a new EIT reconstruction method using null-space decomposition based on a diffusion model (NSDM) is proposed. Specifically, NSDM consists of a forward diffusion process that first gradually adds Gaussian noise to a clean conductivity image, followed by a backward process that learns to predict the noise that should be removed during each sampling step, utilizing a prior to ensure that the denoising process does not deviate from the correct direction. NSDM requires no training, no optimization, and only requires a pre-prepared diffusion model. Experimental results (both simulation and actual tests) demonstrate that the proposed method outperforms existing generation methods and provides higher quality reconstruction, providing a new solution for robotic tactile sensing in real scenarios.

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IJCAI Conference 2024 Conference Paper

A New Guaranteed Outlier Removal Method Based on Plane Constraints for Large-Scale LiDAR Point Cloud Registration

Gang Ma
Hui Wei
Runfeng Lin
Jialiang Wu

In this paper, we present a novel registration method based on plane constraints for large-scale LiDAR point clouds, effectively decoupling rotation estimation and translation estimation. For rotation estimation, we propose an outlier removal method that combines coarse filtering with rotation-invariant constraints and refined filtering based on computational geometric consistency checks, effectively pruning outliers and robustly estimating accurate relative rotations from plane normals. In translation estimation, we propose a component-wise method based on plane translation constraints to efficiently estimate relative translations. The robustness and effectiveness of our proposed method are empirically validated on three popular LiDAR point cloud datasets. The experimental results convincingly demonstrate that our approach achieves state-of-the-art performance.

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IS Journal 2014 Journal Article

Computational Cognitive Models for Brain-Machine Collaborations

Zhongzhi Shi
Jianhua Zhang
Xi Yang
Gang Ma
Baoyuan Qi
Jinpeng Yue

Cyborg intelligence will integrate the best of both machine and biological intelligences via brain-machine integration. To make this integration effective and coadaptive, multiagents should work collaboratively. Here, three levels of computational cognitive models for brain-machine collaboration are presented--awareness-based, motivational-based, and joint-intention-based collaboration. Each collaboration level has its own principle and method.

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