Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

The wide deployment of multi-modal sensors in various areas generates vast amounts of
data with characteristics of high volume, wide variety, and high integrity. However, traditional …

Human-machine interfaces for capturing, conveying, and sharing tactile information across
time and space hold immense potential for healthcare, augmented and virtual reality, human …

Calls for representation in artificial intelligence (AI) and machine learning (ML) are
widespread, with" representation" or" representativeness" generally understood to be both …

The sports industry is witnessing an increasing trend of utilizing multiple synchronized
sensors for player data collection, enabling personalized training systems with multi …

Tactile sensing plays a pivotal role in achieving precise physical manipulation tasks and
extracting vital physical features. This comprehensive review paper presents an in-depth …

Federated learning (FL) is a distributed machine learning (ML) paradigm that enables clients
to collaborate without accessing, infringing upon, or leaking original user data by sharing …

Recent advancements in soft electronic skin (e-skin) have led to the development of human-
like devices that reproduce the skin's functions and physical attributes. These devices are …

With the goal of developing fully autonomous cooking robots, developing robust systems
that can chop a wide variety of objects is important. Existing approaches focus primarily on …

Understanding how we grasp objects with our hands has important applications in areas like
robotics and mixed reality. However this challenging problem requires accurate modeling of …