Search Results for "framework-arduinoststm32"

Isaac Lab is an open-source modular robotics learning framework built atop Isaac Sim. It simplifies research workflows across reinforcement learning, imitation learning, and motion planning by offering robust, GPU-accelerated simulation with realistic sensor and physics fidelity—ideal for sim-to-real robot training. Compatible and optimized for use with Isaac Sim versions (e.g., Sim 5.0 and 4.5). GPU-accelerated, high-fidelity physics and sensor simulation suitable for complex learning tasks...

HomeRobot is an open source robotics framework developed by Facebook Research (Meta AI) that provides a complete software stack for mobile manipulation in real-world and simulated environments. Designed for low-cost robots such as the Hello Robot Stretch, HomeRobot enables agents to perceive, navigate, and interact with their surroundings through vision and manipulation. The system focuses on Open Vocabulary Mobile Manipulation (OVMM) — a challenging task in which a robot must explore...

RoboComp is a robotics framework providing a set of open-source, distributed, real-time robotic and artificial vision software components and the necessary tools to create and manage them.

The RESPOND-R framework is a modular, distributed data logging framework built for the purpose of studying Emergency Informatics as it relates to human robot performance during disaster responses. Tutorials can be found on the wiki: https://sourceforge.net/p/respondr/wiki/

LightningROS is a ROS package implementing the Lightning Path Planning Framework. The code here was developed for ROS Feurte. For later ROS versions, see https://github.com/WPI-ARC/lightning_ros This approach uses a path library to store previous experience while allowing generality by also planning from scratch. Please see the paper below for more details: A Robot Path Planning Framework that Learns from Experience Dmitry Berenson, Pieter Abbeel, and Ken Goldberg IEEE...

A platform independent framework to remote control Lego Mindstorms NXT from Python. Using PyBluez http://org.csail.mit.edu/pybluez/ for Bluetooth connectivity and wxPython http://www.wxpython.org/ for UI.