3 citations · 4 across the 6 of their papers we have counts for
9 papers
Humanoid Mobility via Attention-based Contacts
Junzhe He, Kehan Wen, Chong Zhang +7
Humanoid mobility over complex terrain requires accurate foot placement and coordinated whole-body motion capable of robust adaptation. Reinforcement learning (RL) policies trained…
Bidirectional Voronoi-biased Exploration Curriculum for Reinforcement Learning
Juri Pfammatter, Kaixian Qu, Clemens Schwarke +2
Long-horizon tasks with sparse rewards pose an exploration bottleneck for goal-conditioned reinforcement learning: a policy started from the initial state rarely reaches the goal a…
Bundled Contact Gradients: Stabilizing Differentiable Simulation for Deployable Dynamic Tasks
Dyuman Aditya, Jin Cheng, Clemens Schwarke +4
Differentiable simulation provides analytic gradients of robot dynamics, enabling fast and sample-efficient first-order policy optimization. However, obtaining smooth and informati…
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…
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…
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…
DiffSim2Real: Deploying Quadrupedal Locomotion Policies Purely Trained in Differentiable Simulation
Joshua Bagajo, Clemens Schwarke, Victor Klemm +5
Differentiable simulators provide analytic gradients, enabling more sample-efficient learning algorithms and paving the way for data intensive learning tasks such as learning from…
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…