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

Learning generic skills for humanoid robots interacting with 3D scenes by mimicking human
data is a key research challenge with significant implications for robotics and real-world …

The efficacy of reinforcement learning for robot control relies on the tailored integration of
task-specific priors and heuristics for effective exploration, which challenges their …

Achieving athletic loco-manipulation on robots requires moving beyond traditional tracking
rewards-which simply guide the robot along a reference trajectory-to task rewards that drive …

Extracting physically plausible 3D human motion from videos is a critical task. Although
existing simulation-based motion imitation methods can enhance the physical quality of …

Unsupervised reinforcement learning (RL) aims at pre-training models that can solve a wide
range of downstream tasks in complex environments. Despite recent advancements …

We propose MuJoCo Manipulus, a novel open-source benchmark powered by the MuJoCo
physics simulation engine, designed to accelerate advances in robot learning for tool …