Soft robots have garnered interest for real-world applications because of their intrinsic safety
embedded at the material level. These robots use deformable materials capable of shape …

Continual learning (CL) is a particular machine learning paradigm where the data
distribution and learning objective change through time, or where all the training data and …

In reinforcement learning (RL), representation learning is a proven tool for complex image-
based tasks, but is often overlooked for environments with low-level states, such as physical …

A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature
relevance techniques to explain a deep neural network (DNN) output or explaining models …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

Tactile information plays a critical role in human dexterity. It reveals useful contact
information that may not be inferred directly from vision. In fact, humans can even perform in …

Tactile sensing is a key sensor modality for robots interacting with their surroundings. These
sensors provide a rich and diverse set of data signals that contain detailed information …

Contact-rich manipulation tasks in unstructured environments often require both haptic and
visual feedback. However, it is non-trivial to manually design a robot controller that …

State representation learning, or the ability to capture latent generative factors of an
environment is crucial for building intelligent agents that can perform a wide variety of tasks …

Abstract Representation learning algorithms are designed to learn abstract features that
characterize data. State representation learning (SRL) focuses on a particular kind of …