254 citations · 541 across the 10 of their papers we have counts for
15 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…
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…
Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning
Nikita Rudin, Junzhe He, Joshua Aurand +1
Legged robots are well-suited for navigating terrains inaccessible to wheeled robots, making them ideal for applications in search and rescue or space exploration. However, current…
Symmetry Considerations for Learning Task Symmetric Robot Policies
Mayank Mittal, Nikita Rudin, Victor Klemm +2
Symmetry is a fundamental aspect of many real-world robotic tasks. However, current deep reinforcement learning (DRL) approaches can seldom harness and exploit symmetry effectively…
SpaceHopper: A Small-Scale Legged Robot for Exploring Low-Gravity Celestial Bodies
Alexander Spiridonov, Fabio Buehler, Moriz Berclaz +12
We present SpaceHopper, a three-legged, small-scale robot designed for future mobile exploration of asteroids and moons. The robot weighs 5.2kg and has a body size of 245mm while u…
Learning Agile Locomotion on Risky Terrains
Chong Zhang, Nikita Rudin, David Hoeller +1
Quadruped robots have shown remarkable mobility on various terrains through reinforcement learning. Yet, in the presence of sparse footholds and risky terrains such as stepping sto…
Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End
Jin Jin, Chong Zhang, Jonas Frey +4
Autonomous robots must navigate reliably in unknown environments even under compromised exteroceptive perception, or perception failures. Such failures often occur when harsh envir…
ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots
David Hoeller, Nikita Rudin, Dhionis Sako +1
Performing agile navigation with four-legged robots is a challenging task due to the highly dynamic motions, contacts with various parts of the robot, and the limited field of view…
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
Mayank Mittal, Calvin Yu, Qinxi Yu +12
We present Orbit, a unified and modular framework for robot learning powered by NVIDIA Isaac Sim. It offers a modular design to easily and efficiently create robotic environments w…
Advanced Skills by Learning Locomotion and Local Navigation End-to-End
Nikita Rudin, David Hoeller, Marko Bjelonic +1
The common approach for local navigation on challenging environments with legged robots requires path planning, path following and locomotion, which usually requires a locomotion c…
Neural Scene Representation for Locomotion on Structured Terrain
David Hoeller, Nikita Rudin, Christopher Choy +2
We propose a learning-based method to reconstruct the local terrain for locomotion with a mobile robot traversing urban environments. Using a stream of depth measurements from the…
Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning
Eric Vollenweider, Marko Bjelonic, Victor Klemm +3
In recent years, reinforcement learning (RL) has shown outstanding performance for locomotion control of highly articulated robotic systems. Such approaches typically involve tedio…
Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning
Nikita Rudin, David Hoeller, Philipp Reist +1
In this work, we present and study a training set-up that achieves fast policy generation for real-world robotic tasks by using massive parallelism on a single workstation GPU. We…
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo +8
Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. Both physics simulation and the neural network policy tr…
Cat-like Jumping and Landing of Legged Robots in Low-gravity Using Deep Reinforcement Learning
Nikita Rudin, Hendrik Kolvenbach, Vassilios Tsounis +1
In this article, we show that learned policies can be applied to solve legged locomotion control tasks with extensive flight phases, such as those encountered in space exploration.…