Agile-legged robots have proven to be highly effective in navigating and performing tasks in
complex and challenging environments, including disaster zones and industrial settings …

Models trained with Deep Reinforcement learning (DRL) have been deployed in various
areas of robotics with varying degree of success. To overcome the limitations of data …

Reinforcement learning has a demand for massive data in complex problems, which makes
it infeasible to be applied to real cases where sampling is difficult. The key to coping with …

Large Language Models (LLMs) pre-trained on internet-scale datasets have shown
impressive capabilities in code understanding, synthesis, and general purpose question …

We propose automatic optimisation methods considering the geometry of matrix manifold for
the normalised parameters of neural networks. Layerwise weight normalisation with respect …

The deployment of agile autonomous systems in challenging, unstructured environments
requires adaptation capabilities and robustness to uncertainties. Existing robust and …

Quadrotors, while versatile, are vulnerable to unpredictable environmental disturbances,
including turbulence, gusts, and ground effects, making precise path-following control a …

This paper proposes a new distributed formation control scheme for multiple quadrotors with
uncertainties, which is more practical for real implementation. The proposed design takes …

Existing robust model predictive control (MPC) and vision-based state estimation algorithms
for agile flight, while achieving impressive performance, still demand significant onboard …

Controlling the locomotion of autonomous legged robots necessitates sophisticated
techniques to address system constraints, imposing significant computational demands that …