  A pivotal challenge in robotics is achieving fast, safe, and robust dexterous
grasping across a diverse range of objects, an important goal within industrial
applications. However, existing methods often have very limited speed,
dexterity, and generality, along with limited or no hardware safety guarantees.
In this work, we introduce DextrAH-G, a depth-based dexterous grasping policy
trained entirely in simulation that combines reinforcement learning, geometric
fabrics, and teacher-student distillation. We address key challenges in joint
arm-hand policy learning, such as high-dimensional observation and action
spaces, the sim2real gap, collision avoidance, and hardware constraints.
DextrAH-G enables a 23 motor arm-hand robot to safely and continuously grasp
and transport a large variety of objects at high speed using multi-modal inputs
including depth images, allowing generalization across object geometry. Videos
at https://sites.google.com/view/dextrah-g.
