3 citations · 3 across the 1 of their papers we have counts for
5 papers · 1 filter
Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
NVIDIA, :, Mayank Mittal +104
We present Isaac Lab, the natural successor to Isaac Gym, which extends the paradigm of GPU-native robotics simulation into the era of large-scale multi-modal learning. Isaac Lab c…
End-to-end RL Improves Dexterous Grasping Policies
Ritvik Singh, Karl Van Wyk, Pieter Abbeel +3
This work explores techniques to scale up image-based end-to-end learning for dexterous grasping with an arm + hand system. Unlike state-based RL, vision-based RL is much more memo…
DextrAH-RGB: Visuomotor Policies to Grasp Anything with Dexterous Hands
Ritvik Singh, Arthur Allshire, Ankur Handa +2
One of the most important, yet challenging, skills for a dexterous robot is grasping a diverse range of objects. Much of the prior work has been limited by speed, generality, or re…
DextrAH-G: Pixels-to-Action Dexterous Arm-Hand Grasping with Geometric Fabrics
Tyler Ga Wei Lum, Martin Matak, Viktor Makoviychuk +5
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,…
Geometric Fabrics: a Safe Guiding Medium for Policy Learning
Karl Van Wyk, Ankur Handa, Viktor Makoviychuk +3
Robotics policies are always subjected to complex, second order dynamics that entangle their actions with resulting states. In reinforcement learning (RL) contexts, policies have t…