@article{Guo2024ReconstructingSoft,

title = {Reconstructing Soft Robotic Touch via In-Finger Vision},

author = {Ning Guo and Xudong Han and Shuqiao Zhong and Zhiyuan Zhou and Jian Lin and Fang Wan and Chaoyang Song},

doi = {10.1002/aisy.202400022},

year  = {2024},

date = {2024-10-01},

urldate = {2024-10-01},

journal = {Advanced Intelligent Systems},

volume = {6},

number = {10},

issue = {October},

pages = {2400022},

abstract = {Incorporating authentic tactile interactions into virtual environments presents a notable challenge for the emerging development of soft robotic metamaterials. In this study, a vision-based approach is introduced to learning proprioceptive interactions by simultaneously reconstructing the shape and touch of a soft robotic metamaterial (SRM) during physical engagements. The SRM design is optimized to the size of a finger with enhanced adaptability in 3D interactions while incorporating a see-through viewing field inside, which can be visually captured by a miniature camera underneath to provide a rich set of image features for touch digitization. Employing constrained geometric optimization, the proprioceptive process with aggregated multi-handles is modeled. This approach facilitates real-time, precise, and realistic estimations of the finger's mesh deformation within a virtual environment. Herein, a data-driven learning model is also proposed to estimate touch positions, achieving reliable results with impressive R2 scores of 0.9681, 0.9415, and 0.9541 along the x, y, and z axes. Furthermore, the robust performance of the proposed methods in touch-based human–cybernetic interfaces and human–robot collaborative grasping is demonstrated. In this study, the door is opened to future applications in touch-based digital twin interactions through vision-based soft proprioception.},

keywords = {Authorship - Corresponding, Award - Front Cover, JCR Q1, Jour - Adv. Intell. Syst. (AIS)},

pubstate = {published},

tppubtype = {article}

}

@article{Guo2024AutoencodingA,

title = {Autoencoding a Soft Touch to Learn Grasping from On-land to Underwater},

author = {Ning Guo and Xudong Han and Xiaobo Liu and Shuqiao Zhong and Zhiyuan Zhou and Jian Lin and Jiansheng Dai and Fang Wan and Chaoyang Song},

doi = {10.1002/aisy.202300382},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Advanced Intelligent Systems},

volume = {6},

number = {1},

issue = {January},

pages = {2300382},

abstract = {Robots play a critical role as the physical agent of human operators in exploring the ocean. However, it remains challenging to grasp objects reliably while fully submerging under a highly pressurized aquatic environment with little visible light, mainly due to the fluidic interference on the tactile mechanics between the finger and object surfaces. This study investigates the transferability of grasping knowledge from on-land to underwater via a vision-based soft robotic finger that learns 6D forces and torques (FT) using a supervised variational autoencoder (SVAE). A high-framerate camera captures the whole-body deformations while a soft robotic finger interacts with physical objects on-land and underwater. Results show that the trained SVAE model learns a series of latent representations of the soft mechanics transferable from land to water, presenting a superior adaptation to the changing environments against commercial FT sensors. Soft, delicate, and reactive grasping enabled by tactile intelligence enhances the gripper's underwater interaction with improved reliability and robustness at a much-reduced cost, paving the path for learning-based intelligent grasping to support fundamental scientific discoveries in environmental and ocean research.},

keywords = {Authorship - Corresponding, Award - Front Cover, JCR Q1, Jour - Adv. Intell. Syst. (AIS)},

pubstate = {published},

tppubtype = {article}

}