The success of swarm robotics depends on the precision and reliability of the sensors they
use, as well as the accuracy of their communication links and technologies. However, these …

This letter presents a new learning framework that leverages the knowledge from imitation
learning, deep reinforcement learning, and control theories to achieve human-style …

Recent success of machine learning in many domains has been overwhelming, which often
leads to false expectations regarding the capabilities of behavior learning in robotics. In this …

This paper presents a hierarchical framework for Deep Reinforcement Learning that
acquires motor skills for a variety of push recovery and balancing behaviors, ie, ankle, hip …

This paper presents a deep learning framework that is capable of solving partially
observable locomotion tasks based on our novel interpretation of Recurrent Deterministic …

The development of a robust and versatile biped walking engine might be considered one of
the hardest problems in Mobile Robotics. Even well-developed cities contains obstacles that …

This paper proposes a novel end-to-end controller for the lower-limb exoskeleton for human
performance augmentation (LEHPA) systems based on deep reinforcement learning …

Balancing and push-recovery are essential capabilities enabling humanoid robots to solve
complex locomotion tasks. In this context, classical control systems tend to be based on …

Abstract The transition to Electric Vehicles (EVs) for reducing urban greenhouse gas
emissions is hindered by the lack of public charging infrastructure, particularly fast-charging …

Six-Iegged robots have higher stability and balance, which helps them face more complex
terrain conditions, such as sand, swamp, mine and so forth. Therefore, it is necessary to …