55 citations · 83 across the 4 of their papers we have counts for
7 papers
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…
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…
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…