With the widespread adoption of deep learning, reinforcement learning (RL) has
experienced a dramatic increase in popularity, scaling to previously intractable problems …

Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …

A control system of multiple unmanned aerial vehicles (multi-UAV) is generally very complex
when they complete a task in a closely-cooperative manner, eg two UAVs cooperatively …

Generating locomotive gaits is a very important work for bioinspired robots and has received
wide attentions among scientists and engineers. The central pattern generator (CPG) neural …

Unmanned aerial vehicle (UAV) has been widely deployed in efficient data collection for
Internet of Things (IoT) networks. Information freshness in data collection can be …

Deep reinforcement learning (DRL) and evolution strategies (ESs) have surpassed human-
level control in many sequential decision-making problems, yet many open challenges still …

We propose a method to estimate the mechanical parameters of fabrics using a casual
capture setup with a depth camera. Our approach enables to create mechanically‐correct …

We present a hierarchical deep reinforcement learning (DRL) framework with prominent
sampling efficiency and sim-to-real transfer ability for fast and safe navigation: the low-level …

With the increasing demand for the dexterity of robotic operation, dexterous manipulation of
multi-fingered robotic hands with reinforcement learning is an interesting subject in the field …

With the widespread application of industrial robots, there is growing interest in optimizing
their energy consumption during motion processes. Traditional methods typically rely on …