3 citations · 5 across the 4 of their papers we have counts for
5 papers
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…
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…
Divide, Discover, Deploy: Factorized Skill Learning with Symmetry and Style Priors
Rafael Cathomen, Mayank Mittal, Marin Vlastelica +1
Unsupervised Skill Discovery (USD) allows agents to autonomously learn diverse behaviors without task-specific rewards. While recent USD methods have shown promise, their applicati…
A Pragmatist Robot: Learning to Plan Tasks by Experiencing the Real World
Kaixian Qu, Guowei Lan, René Zurbrügg +4
Large language models (LLMs) have emerged as the dominant paradigm for robotic task planning using natural language instructions. However, trained on general internet data, LLMs ar…