254 citations · 612 across the 14 of their papers we have counts for
17 papers
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…
Spatially-Enhanced Recurrent Memory for Long-Range Mapless Navigation via End-to-End Reinforcement Learning
Fan Yang, Per Frivik, David Hoeller +3
Recent advancements in robot navigation, particularly with end-to-end learning approaches such as reinforcement learning (RL), have demonstrated strong performance. However, succes…
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…
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…
Locomotion Policy Guided Traversability Learning using Volumetric Representations of Complex Environments
Jonas Frey, David Hoeller, Shehryar Khattak +1
Despite the progress in legged robotic locomotion, autonomous navigation in unknown environments remains an open problem. Ideally, the navigation system utilizes the full potential…
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…
Learning a State Representation and Navigation in Cluttered and Dynamic Environments
David Hoeller, Lorenz Wellhausen, Farbod Farshidian +1
In this work, we present a learning-based pipeline to realise local navigation with a quadrupedal robot in cluttered environments with static and dynamic obstacles. Given high-leve…
Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation
Mayank Mittal, David Hoeller, Farbod Farshidian +2
A kitchen assistant needs to operate human-scale objects, such as cabinets and ovens, in unmapped environments with dynamic obstacles. Autonomous interactions in such environments…
Joint Space Control via Deep Reinforcement Learning
Visak Kumar, David Hoeller, Balakumar Sundaralingam +2
The dominant way to control a robot manipulator uses hand-crafted differential equations leveraging some form of inverse kinematics / dynamics. We propose a simple, versatile joint…
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion
Xingye Da, Zhaoming Xie, David Hoeller +5
We present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped (the Unitree Laikago). The sy…
Practical Reinforcement Learning For MPC: Learning from sparse objectives in under an hour on a real robot
Napat Karnchanachari, Miguel I. Valls, David Hoeller +1
Model Predictive Control (MPC) is a powerful control technique that handles constraints, takes the system's dynamics into account, and optimizes for a given cost function. In pract…
Deep Value Model Predictive Control
Farbod Farshidian, David Hoeller, Marco Hutter
In this paper, we introduce an actor-critic algorithm called Deep Value Model Predictive Control (DMPC), which combines model-based trajectory optimization with value function esti…