Robotic grippers can be equipped with tactile sensing systems to extract information from a
manipulated object. The real-time classification of the physical properties of a grasped …

Abstract Machine Learning (ML) algorithms are generally designed for scenarios in which all
data is stored in one data center, where the training is performed. However, in many …

Modern myoelectric prostheses can perform multiple functions (eg, several grasp types and
wrist rotation) but their intuitive control by the user is still an open challenge. It has been …

We consider the task of object gras** with a prosthetic hand capable of multiple grasp
types. In this setting, communicating the intended grasp type often requires a high user …

Affordance segmentation is used to split object images into parts according to the possible
interactions, usually to drive safe robotic gras**. Most approaches to affordance …

The current trend of applying transfer learning from convolutional neural networks (CNNs)
trained on large datasets can be an overkill when the target application is a custom and …

Visual affordance segmentation identifies the surfaces of an object an agent can interact
with. Common challenges for the identification of affordances are the variety of the geometry …

Affordance detection consists in predicting the possibility of a specific action on an object.
While this problem is generally defined for fully autonomous robotic platforms, we are …

Depth sensors play a major role in the control pipeline of semi-autonomous prostheses. The
feed from these sensors can complement, or even substitute, the information retrieved by …

Robust landing pad detection plays a major role in Autonomous Unmanned Aerial Vehicles
(UAVs). This problem can be approached using deep neural networks for vision-based …