In recent years, the integration of large language models (LLMs) has revolutionized the field
of robotics, enabling robots to communicate, understand, and reason with human-like …

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 …

Humanoid robots have great potential to perform various human-level skills. These skills
involve locomotion, manipulation, and cognitive capabilities. Driven by advances in machine …

Accurate motion control in the face of disturbances within complex environments remains a
major challenge in robotics. Classical model-based approaches often struggle with …

We present a large language model (LLM) based system to empower quadrupedal robots
with problem-solving abilities for long-horizon tasks beyond short-term motions. Long …

This work introduces DiffuseLoco, a framework for training multi-skill diffusion-based policies
for dynamic legged locomotion from offline datasets, enabling real-time control of diverse …

Model-free reinforcement learning is a promising approach for autonomously solving
challenging robotics control problems, but faces exploration difficulty without information of …

Sim-to-real transfer remains a significant challenge in robotics due to the discrepancies
between simulated and real-world dynamics. Traditional methods like Domain …