Embodied Artificial Intelligence (Embodied AI) is crucial for achieving Artificial General
Intelligence (AGI) and serves as a foundation for various applications that bridge cyberspace …

Most policy search (PS) algorithms require thousands of training episodes to find an
effective policy, which is often infeasible with a physical robot. This survey article focuses on …

Parkour is a grand challenge for legged locomotion that requires robots to overcome various
obstacles rapidly in complex environments. Existing methods can generate either diverse …

Bayesian optimization provides sample-efficient global optimization for a broad range of
applications, including automatic machine learning, engineering, physics, and experimental …

Designing agile locomotion for quadruped robots often requires extensive expertise and
tedious manual tuning. In this paper, we present a system to automate this process by …

We study the problem of mobile manipulation using legged robots equipped with an arm,
namely legged loco-manipulation. The robot legs, while usually utilized for mobility, offer an …

Bayesian optimization (BO) is a popular approach to optimize expensive-to-evaluate black-
box functions. A significant challenge in BO is to scale to high-dimensional parameter …

Bayesian optimization is a data-efficient technique which can be used for control parameter
tuning, parametric policy adaptation, and structure design in robotics. Many of these …

Bayesian optimization (BO) recently became popular in robotics to optimize control
parameters and parametric policies in direct reinforcement learning due to its data efficiency …

Robotics controllers often consist of expert-designed heuristics, which can be hard to tune in
higher dimensions. Simulation can aid in optimizing these controllers if parameters learned …