Congratulations on Hongliang Zhao. His research work has been accepted by ICRA 2026.

Congratulations on Hongliang Zhao, whose research paper on the meta-learning for tactile perception has been accepted by ICRA 2026. 
IEEE International Conference on Robotics and Automation (ICRA) is one of the most famous international conference in robotics community. In 2026, it will be held on 1–5 June in Vienna, Austria. According to the official statistical data, the acceptance rate was only 38.04%.
"CONFERENCE STATISTICS
We received 4947 paper submissions from 86 countries. Of these, 1882 have been selected for presentation at ICRA 2026, which represents an acceptance rate of 38.04%".

Refer to arxiv link https://arxiv.org/abs/2603.08423  for details of the paper and the pramiry information is listed as follows:

Tactile Recognition of Both Shapes and Materials with Automatic Feature Optimization-Enabled Meta Learning.

Hongliang Zhao, Wenhui Yang, Yang Chen, Zhuorui Wang, Baiheng Liu, Longhui Qin

Tactile perception is indispensable for robots to implement various manipulations dexterously, especially in contact-rich scenarios. However, alongside the development of deep learning techniques, it meanwhile suffers from training data scarcity and a time-consuming learning process in practical applications since the collection of a large amount of tactile data is costly and sometimes even impossible. Hence, we propose an automatic feature optimization-enabled prototypical network to realize meta-learning, i.e., AFOP-ML framework. As a ``learn to learn" network, it not only adapts to new unseen classes rapidly with few-shot, but also learns how to determine the optimal feature space automatically. Based on the four-channel signals acquired from a tactile finger, both shapes and materials are recognized. On a 36-category benchmark, it outperforms several existing approaches by attaining an accuracy of 96.08% in 5-way-1-shot scenario, where only 1 example is available for training. It still remains 88.7% in the extreme 36-way-1-shot case. The generalization ability is further validated through three groups of experiment involving unseen shapes, materials and force/speed perturbations. More insights are additionally provided by this work for the interpretation of recognition tasks and improved design of tactile sensors.

by Longhui Qin

Mar 10, 2026