no code implementations • 1 Feb 2024 • Donghoon Youm, Hyunsik Oh, Suyoung Choi, Hyeongjun Kim, Jemin Hwangbo
This paper introduces a novel proprioceptive state estimator for legged robots that combines model-based filters and deep neural networks.
no code implementations • 7 Sep 2023 • HUI ZHANG, Sammy Christen, Zicong Fan, Luocheng Zheng, Jemin Hwangbo, Jie Song, Otmar Hilliges
ArtiGrasp leverages reinforcement learning and physics simulations to train a policy that controls the global and local hand pose.
no code implementations • 24 Aug 2023 • Yunho Kim, Hyunsik Oh, Jeonghyun Lee, Jinhyeok Choi, Gwanghyeon Ji, Moonkyu Jung, Donghoon Youm, Jemin Hwangbo
In this work, we propose a novel reinforcement learning framework for training neural network controllers for complex robotic systems consisting of both rewards and constraints.
1 code implementation • 19 Apr 2022 • Yunho Kim, Chanyoung Kim, Jemin Hwangbo
For autonomous quadruped robot navigation in various complex environments, a typical SOTA system is composed of four main modules -- mapper, global planner, local planner, and command-tracking controller -- in a hierarchical manner.
1 code implementation • 11 Feb 2022 • Gwanghyeon Ji, Juhyeok Mun, Hyeongjun Kim, Jemin Hwangbo
In this paper, we propose a locomotion training framework where a control policy and a state estimator are trained concurrently.
1 code implementation • CVPR 2022 • Sammy Christen, Muhammed Kocabas, Emre Aksan, Jemin Hwangbo, Jie Song, Otmar Hilliges
We introduce the dynamic grasp synthesis task: given an object with a known 6D pose and a grasp reference, our goal is to generate motions that move the object to a target 6D pose.
1 code implementation • 21 Oct 2020 • Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, Marco Hutter
The trained controller has taken two generations of quadrupedal ANYmal robots to a variety of natural environments that are beyond the reach of prior published work in legged locomotion.
2 code implementations • 24 Jan 2019 • Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy, Dario Bellicoso, Vassilios Tsounis, Vladlen Koltun, Marco Hutter
In the present work, we introduce a method for training a neural network policy in simulation and transferring it to a state-of-the-art legged system, thereby leveraging fast, automated, and cost-effective data generation schemes.
no code implementations • 22 Jan 2019 • Joonho Lee, Jemin Hwangbo, Marco Hutter
We experimentally validate our approach on the quadrupedal robot ANYmal, which is a dog-sized quadrupedal system with 12 degrees of freedom.
1 code implementation • 17 Jul 2017 • Jemin Hwangbo, Inkyu Sa, Roland Siegwart, Marco Hutter
In this paper, we present a method to control a quadrotor with a neural network trained using reinforcement learning techniques.
Robotics