Sensorimotor control of complex, dynamic systems such as humanoids or quadrupedal
robots is notoriously difficult. While artificial systems traditionally employ hierarchical …

The current state-of-the-art in quadruped locomotion is able to produce a variety of complex
motions. These methods either rely on switching between a discrete set of skills or learn a …

This work developed a kernel-based residual learning framework for quadrupedal robotic
locomotion. Ini-tially, a kernel neural network is trained with data collected from an MPC …

This paper explores the application of Variational Autoencoders (VAEs) to sparse non-visual
data, focusing on a case study involving proximity sensors on a mobile robot. Traditionally …

This dissertation addresses the integration of reinforcement learning (RL) with model-based
methods to develop robust and efficient bipedal locomotion strategies for robots, highlighting …

The current state-of-the-art in quadruped locomotion is able to produce robust motion for
terrain traversal but requires the segmentation of a desired trajectory into a discrete set of …

Oscillatory signals drive reaching and locomotion for both robots and primates. Recent
results in neuroscience have shown that periodic signals are present in the motor cortex of …