Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …

Imitation learning from human demonstrations has shown impressive performance in
robotics. However, most results focus on table-top manipulation, lacking the mobility and …

Remarkable progress has been made in recent years in the fields of vision, language, and
robotics. We now have vision models capable of recognizing objects based on language …

Parkour is a grand challenge for legged locomotion that requires robots to overcome various
obstacles rapidly in complex environments. Existing methods can generate either diverse …

Understanding how humans leverage semantic knowledge to navigate unfamiliar
environments and decide where to explore next is pivotal for developing robots capable of …

We study the problem of mobile manipulation using legged robots equipped with an arm,
namely legged loco-manipulation. The robot legs, while usually utilized for mobility, offer an …

We introduce UMI-on-Legs, a new framework that combines real-world and simulation data
for quadruped manipulation systems. We scale task-centric data collection in the real world …

Mobile manipulation constitutes a fundamental task for robotic assistants and garners
significant attention within the robotics community. A critical challenge inherent in mobile …

We present PoliFormer (Policy Transformer), an RGB-only indoor navigation agent trained
end-to-end with reinforcement learning at scale that generalizes to the real-world without …

Deploying robots in open-ended unstructured environments such as homes has been a long-
standing research problem. However, robots are often studied only in closed-off lab settings …