3 citations · 10 across the 8 of their papers we have counts for
9 papers
ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning
Jayjun Lee, Jessica Yin, Asif Rana +8
We introduce Accelerating Dexterity via Pre-Training (ADEPT), a large-scale reinforcement learning (RL) framework for learning sim-to-real transferable dexterity across high degree…
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…
Synthetica: Large Scale Synthetic Data for Robot Perception
Ritvik Singh, Jingzhou Liu, Karl Van Wyk +5
Vision-based object detectors are a crucial basis for robotics applications as they provide valuable information about object localisation in the environment. These need to ensure…
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…
cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation
Balakumar Sundaralingam, Siva Kumar Sastry Hari, Adam Fishman +9
This paper explores the problem of collision-free motion generation for manipulators by formulating it as a global motion optimization problem. We develop a parallel optimization t…
Fabrics: A Foundationally Stable Medium for Encoding Prior Experience
Nathan Ratliff, Karl Van Wyk
Most dynamics functions are not well-aligned to task requirements. Controllers, therefore, often invert the dynamics and reshape it into something more useful. The learning communi…