5 papers
Learning Whole-Body Humanoid Locomotion via Motion Generation and Motion Tracking
Zewei Zhang, Kehan Wen, Michael Xu +7
Whole-body humanoid locomotion is challenging due to high-dimensional control, morphological instability, and the need for real-time adaptation to various terrains using onboard pe…
Reinforcement Learning-Based Control for an Inline Skating Humanoid Robot
Ethan Marot, Thomas Bi, Clemens Schwarke +3
As humanoid robots become increasingly dynamic, coupling them with reinforcement learning offers a promising approach to solving the complex, underactuated mechanics of passive inl…
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…
RSL-RL: A Learning Library for Robotics Research
Clemens Schwarke, Mayank Mittal, Nikita Rudin +2
RSL-RL is an open-source Reinforcement Learning library tailored to the specific needs of the robotics community. Unlike broad general-purpose frameworks, its design philosophy pri…
Learning Deployable Locomotion Control via Differentiable Simulation
Clemens Schwarke, Victor Klemm, Joshua Bagajo +4
Differentiable simulators promise to improve sample efficiency in robot learning by providing analytic gradients of the system dynamics. Yet, their application to contact-rich task…