Robotic equipment has been playing a central role since the proposal of smart
manufacturing. Since the beginning of the first integration of industrial robots into production …

In the past two decades, robotics in construction (RiC) has become an interdisciplinary
research field that integrates a large number of urgent technologies (eg, additive …

We investigated whether deep reinforcement learning (deep RL) is able to synthesize
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …

Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine
learning that has attracted considerable attention in recent years. The goal of XRL is to …

Robotics and neuroscience are sister disciplines that both aim to understand how agile,
efficient, and robust locomotion can be achieved in autonomous agents. Robotics has …

Abstract Continual Semantic Segmentation (CSS) extends static semantic segmentation by
incrementally introducing new classes for training. To alleviate the catastrophic forgetting …

Fast and stable locomotion of legged robots involves demanding and contradictory
requirements, in particular rapid control frequency as well as an accurate dynamics model …

Creating autonomous robots that can actively explore the environment, acquire knowledge
and learn skills continuously is the ultimate achievement envisioned in cognitive and …

In reinforcement learning (RL), a reward function that aligns exactly with a task's true
performance metric is often necessarily sparse. For example, a true task metric might …

A collaborative robot, or cobot, enables users to work closely with it through direct
communication without the use of traditional barricades. Cobots eliminate the gap that has …