This article is an historical overview of control theory applied to robotic manipulators, with an
emphasis on the early fundamental theoretical foundations of robot control. It discusses …

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 …

The state-of-the-art reinforcement learning (RL) techniques have made innumerable
advancements in robot control, especially in combination with deep neural networks …

Deep reinforcement learning (DRL) is a promising approach for developing legged
locomotion skills. However, current work commonly describes DRL as being a one-shot …

We focus on the problem of developing energy efficient controllers for quadrupedal robots.
Animals can actively switch gaits at different speeds to lower their energy consumption. In …

Understanding the gap between simulation and reality is critical for reinforcement learning
with legged robots, which are largely trained in simulation. However, recent work has …

Learning adaptable policies is crucial for robots to operate autonomously in our complex
and quickly changing world. In this work, we present a new meta-learning method that …

In this paper, a hierarchical and robust framework for learning bipedal locomotion is
presented and successfully implemented on the 3D biped robot Digit built by Agility …

Abstract Model-free reinforcement learning (RL) for legged locomotion commonly relies on a
physics simulator that can accurately predict the behaviors of every degree of freedom of the …

Deep reinforcement learning (DRL) is a promising approach for developing legged
locomotion skills. However, the iterative design process that is inevitable in practice is poorly …