Learning with pyCub: A Simulation and Exercise Framework for Humanoid Robotics

Fuente: arXiv
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Main Authors: Rustler, Lukas, Hoffmann, Matej
Format: Preprint
Published: 2025
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author Rustler, Lukas
Hoffmann, Matej
author_facet Rustler, Lukas
Hoffmann, Matej
contents We present pyCub, an open-source physics-based simulation of the humanoid robot iCub, along with exercises to teach students the basics of humanoid robotics. Compared to existing iCub simulators (iCub SIM, iCub Gazebo), which require C++ code and YARP as middleware, pyCub works without YARP and with Python code. The complete robot with all articulations has been simulated, with two cameras in the eyes and the unique sensitive skin of the iCub comprising 4000 receptors on its body surface. The exercises range from basic control of the robot in velocity, joint, and Cartesian space to more complex tasks like gazing, grasping, or reactive control. The whole framework is written and controlled with Python, thus allowing to be used even by people with small or almost no programming practice. The exercises can be scaled to different difficulty levels. We tested the framework in two runs of a course on humanoid robotics. The simulation, exercises, documentation, Docker images, and example videos are publicly available at https://rustlluk.github.io/pyCub.
format Preprint
id arxiv_https___arxiv_org_abs_2506_01756
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Learning with pyCub: A Simulation and Exercise Framework for Humanoid Robotics
Rustler, Lukas
Hoffmann, Matej
Robotics
We present pyCub, an open-source physics-based simulation of the humanoid robot iCub, along with exercises to teach students the basics of humanoid robotics. Compared to existing iCub simulators (iCub SIM, iCub Gazebo), which require C++ code and YARP as middleware, pyCub works without YARP and with Python code. The complete robot with all articulations has been simulated, with two cameras in the eyes and the unique sensitive skin of the iCub comprising 4000 receptors on its body surface. The exercises range from basic control of the robot in velocity, joint, and Cartesian space to more complex tasks like gazing, grasping, or reactive control. The whole framework is written and controlled with Python, thus allowing to be used even by people with small or almost no programming practice. The exercises can be scaled to different difficulty levels. We tested the framework in two runs of a course on humanoid robotics. The simulation, exercises, documentation, Docker images, and example videos are publicly available at https://rustlluk.github.io/pyCub.
title Learning with pyCub: A Simulation and Exercise Framework for Humanoid Robotics
topic Robotics
url https://arxiv.org/abs/2506.01756