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 …

We investigated whether deep reinforcement learning (deep RL) is able to synthesize
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …

Humans can perform parkour by traversing obstacles in a highly dynamic fashion requiring
precise eye-muscle coordination and movement. Getting robots to do the same task requires …

Abstract We introduce Efficient Motion Diffusion Model (EMDM) for fast and high-quality
human motion generation. Current state-of-the-art generative diffusion models have …

Controllable human motion synthesis is essential for applications in AR/VR, gaming and
embodied AI. Existing methods often focus solely on either language or full trajectory control …

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

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 …

Humans interact with objects all the time. Enabling a humanoid to learn human-object
interaction (HOI) is a key step for future smart animation and intelligent robotics systems …

Deep reinforcement learning can enable robots to autonomously acquire complex behaviors
such as legged locomotion. However, RL in the real world is complicated by constraints on …