Unmanned aerial vehicle (UAV)-based wireless networks have received increasing
research interest in recent years and are gradually being utilized in various aspects of our …

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 …

Animals are capable of precise and agile locomotion using vision. Replicating this ability
has been a long-standing goal in robotics. The traditional approach has been to decompose …

Humanoid robots that can autonomously operate in diverse environments have the potential
to help address labor shortages in factories, assist elderly at home, and colonize new …

Agile maneuvers such as sprinting and high-speed turning in the wild are challenging for
legged robots. We present an end-to-end learned controller that achieves record agility for …

Successful real-world deployment of legged robots would require them to adapt in real-time
to unseen scenarios like changing terrains, changing payloads, wear and tear. This paper …

Abstract We introduce Habitat 2.0 (H2. 0), a simulation platform for training virtual robots in
interactive 3D environments and complex physics-enabled scenarios. We make …

Motion control is crucial for multilegged robot locomotion and task completion. This study
aims to address the fundamental challenges of inadequate foot tracking and weak leg …

Legged robots that can operate autonomously in remote and hazardous environments will
greatly increase opportunities for exploration into underexplored areas. Exteroceptive …

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …