Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …

Legged locomotion holds the premise of universal mobility, a critical capability for many real-
world robotic applications. Both model-based and learning-based approaches have …

Large language models (LLMs) have demonstrated exciting progress in acquiring diverse
new capabilities through in-context learning, ranging from logical reasoning to code-writing …

This paper presents a comprehensive study on using deep reinforcement learning (RL) to
create dynamic locomotion controllers for bipedal robots. Going beyond focusing on a single …

Learned locomotion policies can rapidly adapt to diverse environments similar to those
experienced during training but lack a mechanism for fast tuning when they fail in an out-of …

Accurate and precise terrain estimation is a difficult problem for robot locomotion in real-
world environments. Thus, it is useful to have systems that do not depend on accurate …

In a world designed for legs, quadrupeds, bipeds, and humanoids have the opportunity to
impact emerging robotics applications from logistics, to agriculture, to home assistance. The …

In this letter, we propose a locomotion training framework where a control policy and a state
estimator are trained concurrently. The framework consists of a policy network which outputs …

Fast and stable locomotion of legged robots involves demanding and contradictory
requirements, in particular rapid control frequency as well as an accurate dynamics model …

Training a high-dimensional simulated agent with an under-specified reward function often
leads the agent to learn physically infeasible strategies that are ineffective when deployed in …