Achieving versatile robot locomotion requires motor skills that can adapt to previously
unseen situations. We propose a multi-expert learning architecture (MELA) that learns to …

Robot motor skills can be acquired by deep reinforcement learning as neural networks to
reflect state–action mapping. The selection of states has been demonstrated to be crucial for …

The current state of the art in cognitive robotics, covering the challenges of building AI-
powered intelligent robots inspired by natural cognitive systems. A novel approach to …

Falling is inevitable for legged robots in challenging real-world scenarios, where
environments are unstructured and situations are unpredictable, such as uneven terrain in …

Electrical resistance tomography (ERT) can be used to create large-scale soft tactile sensors
that are flexible and robust. Good performance requires a fast and accurate mapping from …

For model-free deep reinforcement learning of quadruped locomotion, the initialization of
robot configurations is crucial for data efficiency and robustness. This work focuses on …

A proper movement categorization reduces the complexity of understanding or reproducing
human movements in fields such as physiology, rehabilitation, and robotics, through …

This article provides a brief overview of the technology of humanoid robots. First, historical
development and hardware progress are presented mainly on human-size full-body biped …

In the last two decades the scientific community has shown a great interest in understanding
and shaping the interaction mechanisms between humans and robots. The interaction …

Efficient action prediction is of central importance for the fluent workflow between humans
and equally so for human-robot interaction. To achieve prediction, actions can be …