We present a unified model-based and data-driven approach for quadrupedal planning and
control to achieve dynamic locomotion over uneven terrain. We utilize on-board …

Several earlier studies have shown impressive control performance in complex robotic
systems by designing the controller using a neural network and training it with model-free …

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 …

Reinforcement Learning (RL) has seen many recent successes for quadruped robot control.
The imitation of reference motions provides a simple and powerful prior for guiding solutions …

We investigate the use of prior knowledge of human and animal movement to learn reusable
locomotion skills for real legged robots. Our approach builds upon previous work on …

We present a hierarchical framework that combines model-based control and reinforcement
learning (RL) to synthesize robust controllers for a quadruped (the Unitree Laikago). The …

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 …

In this paper, an adaptive locomotion control approach for a hexapod robot is proposed.
Inspired from biological neuro control systems, a 3D two-layer artificial center pattern …

We present a control architecture for real-time adaptation and tracking of trajectories
generated using a terrain-aware trajectory optimization solver. This approach enables us to …

This letter aims to solve a safe reinforcement learning (RL) problem with risk measure-based
constraints. As risk measures, such as conditional value at risk (CVaR), focus on the tail …