Enabling humanoid robots to clean rooms has long been a pursued dream within humanoid
research communities. However, many tasks require multi-humanoid collaboration, such as …

Humanoid whole-body control requires adapting to diverse tasks such as navigation, loco-
manipulation, and tabletop manipulation, each demanding a different mode of control. For …

An open research question in robotics is how to combine the benefits of model-free
reinforcement learning (RL)—known for its strong task performance and flexibility in …

We introduce MuJoCo Playground, a fully open-source framework for robot learning built
with MJX, with the express goal of streamlining simulation, training, and sim-to-real transfer …

Real-world legged locomotion systems often need to reconcile agility and safety for different
scenarios. Moreover, the underlying dynamics are often unknown and time-variant (eg …

Generative control policies have recently unlocked major progress in robotics. These
methods produce action sequences via diffusion or flow matching, with training data …

Model Predictive Control has emerged as a popular tool for robots to generate complex
motions. However, the real-time requirement has limited the use of hard constraints and …

The recent success of diffusion-based generative models in image and natural language
processing has ignited interest in diffusion-based trajectory optimization for nonlinear control …

Automatic fall recovery is a crucial prerequisite before humanoid robots can be reliably
deployed. Hand-designing controllers for getting up is difficult because of the varied …

Model-based control is a crucial component of robotic navigation. However, it often struggles
with entrapment in local minima due to its inherent nature as a finite, myopic optimization …