Bayesian inference has many advantages in robotic motion planning over four perspectives:
The uncertainty quantification of the policy, safety (risk-aware) and optimum guarantees of …

Mobile Manipulation (MM) systems are ideal candidates for taking up the role of personal
assistants in unstructured real-world environments. Among other challenges, Mobile …

Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage
prior knowledge for accelerated robot learning. Skills are typically extracted from expert …

Achieving accurate control performance of the end-effector is critical for practical
applications of aerial manipulator. However, due to the presence of floating-base …

It is well-known that image quality assessment usually meets with the problem of perception-
distortion (pd) tradeoff. The existing deep image super-resolution (SR) methods either focus …

This study focuses on the numerical analysis and optimal control of vertical-axis wind
turbines (VAWT) using Bayesian reinforcement learning (RL). We specifically address small …

In recent years, Embodied AI has become one of the main topics in robotics. For the agent to
operate in human-centric environments, it needs the ability to explore previously unseen …

The ability to effectively reuse prior knowledge is a key requirement when building general
and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common …

Control of wheeled humanoid locomotion is a challenging problem due to the nonlinear
dynamics and under-actuated characteristics of these robots. Traditionally, feedback …

With the development of intelligent manufacturing, robots are being increasingly applied in
manufacturing systems due to their high flexibility. To avoid production disruptions caused …