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

One of the most interesting and pressing challenges in the study on biped humanoid robots
is to achieve high robustness in locomotion. This paper presents a brief overview of work …

This work presents a Differential Dynamic Programming (DDP) approach for systems
characterized by implicit dynamics using sensitivity analysis, such as those modelled via …

This paper presents a hierarchical framework based on deep reinforcement learning that
naturally acquires control policies that are capable of performing balancing behaviours such …

The humanoid robot, one of the ideal unmanned systems, has the potential to perform tasks
currently carried out by human workers in industry, manufacturing, service, and disaster …

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 …

For real-world applications, a biped robot should maintain stable walking when subjected to
sudden external disturbances. Typically, unexpected changes to body acceleration indicate …

A new approach is developed to control 3-D bipedal motion and balance under disturbance,
called the behavior-based locomotion controller (BBLC). Bipedal walking is divided into …

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 …

This paper proposes a novel nonlinear energy-based coupling control for an underactuated
offshore ship-mounted crane, which guarantees both precise trolley positioning and payload …