Task and motion planning (TAMP) integrates high-level task planning and low-level motion
planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic …

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

In recent years, the transformer architecture has become the de facto standard for machine
learning algorithms applied to natural language processing and computer vision. Despite …

We introduce BiGym, a new benchmark and learning environment for mobile bi-manual
demo-driven robotic manipulation. BiGym features 40 diverse tasks set in home …

We present SMPLOlympics, a collection of physically simulated environments that allow
humanoids to compete in a variety of Olympic sports. Sports simulation offers a rich and …

Humanoid robots are designed to perform diverse loco-manipulation tasks. However, they
face challenges due to their high-dimensional and unstable dynamics, as well as the …

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 …

Training reinforcement learning (RL) agents on robotic tasks typically requires a large
number of training samples. This is because training data often consists of noisy trajectories …

Physical Human-Scene Interaction (HSI) plays a crucial role in numerous applications.
However, existing HSI techniques are limited to specific object dynamics and privileged …

Recent advances in CV and NLP have been largely driven by scaling up the number of
network parameters, despite traditional theories suggesting that larger networks are prone to …