An historical perspective on the control of robotic manipulators
MW Spong - Annual Review of Control, Robotics, and …, 2022 - annualreviews.org
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 …
emphasis on the early fundamental theoretical foundations of robot control. It discusses …
Multi-expert learning of adaptive legged locomotion
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 …
unseen situations. We propose a multi-expert learning architecture (MELA) that learns to …
A survey of sim-to-real transfer techniques applied to reinforcement learning for bioinspired robots
The state-of-the-art reinforcement learning (RL) techniques have made innumerable
advancements in robot control, especially in combination with deep neural networks …
advancements in robot control, especially in combination with deep neural networks …
Learning locomotion skills for cassie: Iterative design and sim-to-real
Z Xie, P Clary, J Dao, P Morais… - … on Robot Learning, 2020 - proceedings.mlr.press
Deep reinforcement learning (DRL) is a promising approach for developing legged
locomotion skills. However, current work commonly describes DRL as being a one-shot …
locomotion skills. However, current work commonly describes DRL as being a one-shot …
Fast and efficient locomotion via learned gait transitions
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 …
Animals can actively switch gaits at different speeds to lower their energy consumption. In …
Dynamics randomization revisited: A case study for quadrupedal locomotion
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 …
with legged robots, which are largely trained in simulation. However, recent work has …
Rapidly adaptable legged robots via evolutionary meta-learning
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 …
and quickly changing world. In this work, we present a new meta-learning method that …
Robust feedback motion policy design using reinforcement learning on a 3d digit bipedal robot
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 …
presented and successfully implemented on the 3D biped robot Digit built by Agility …
Glide: Generalizable quadrupedal locomotion in diverse environments with a centroidal model
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 …
physics simulator that can accurately predict the behaviors of every degree of freedom of the …
Iterative reinforcement learning based design of dynamic locomotion skills for cassie
Z Xie, P Clary, J Dao, P Morais, J Hurst… - arXiv preprint arXiv …, 2019 - arxiv.org
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 …
locomotion skills. However, the iterative design process that is inevitable in practice is poorly …