The Robot Operating System (ROS) has become the de facto standard middleware in robotics, widely adopted across domains ranging from education to industrial applications. The RoboStack distribution, a conda-based packaging system for ROS, has extended ROS's accessibility by facilitating installation across all major operating systems and architectures, integrating seamlessly with scientific tools such as PyTorch and Open3D. This paper presents ROS2WASM, a novel integration of RoboStack with WebAssembly, enabling the execution of ROS 2 and its associated software directly within web browsers, without requiring local installations. ROS2WASM significantly enhances the reproducibility and shareability of research, lowers barriers to robotics education, and leverages WebAssembly's robust security framework to protect against malicious code. We detail our methodology for cross-compiling ROS 2 packages into WebAssembly, the development of a specialized middleware for ROS 2 communication within browsers, and the implementation of www.ros2wasm.dev, a web platform enabling users to interact with ROS 2 environments. Additionally, we extend support to the Robotics Toolbox for Python and adapt its Swift simulator for browser compatibility. Our work paves the way for unprecedented accessibility in robotics, offering scalable, secure, and reproducible environments that have the potential to transform educational and research paradigms.

Mobile manipulator robots operating in complex domestic and industrial environments must effectively coordinate their base and arm motions while avoiding obstacles. While current reactive control methods gracefully achieve this coordination, they rely on simplified and idealised geometric representations of the environment to avoid collisions. This limits their performance in cluttered environments. To address this problem, we introduce RMMI, a reactive control framework that leverages the ability of neural Signed Distance Fields (SDFs) to provide a continuous and differentiable representation of the environment’s geometry. RMMI formulates a quadratic program that optimises jointly for robot base and arm motion, maximises the manipulability, and avoids collisions through a set of inequality constraints. These constraints are constructed by querying the SDF for the distance and direction to the closest obstacle for a large number of sampling points on the robot. We evaluate RMMI both in simulation and in a set of real-world experiments. For reaching in cluttered environments, we observe a 25% increase in success rate. For additional details, code, and experiment videos, please visit https://rmmi.github.io/.

For 25 years the Robotics Toolbox for MATLAB®has been used for teaching and research worldwide. This paper describes its successor – the Robotics Toolbox for Python. More than just a port, it takes advantage of popular open-source packages and resources to provide platform portability, fast browser-based 3D graphics, quality documentation, fast numerical and symbolic operations, powerful IDEs, shareable and web-browseable notebooks all powered by GitHub and the open-source community. The new Toolbox provides well-known functionality for spatial mathematics (homogeneous transformations, quaternions, triple angles and twists), trajectories, kinematics (zeroth to second order), dynamics and a rich assortment of robot models. In addition, we’ve taken the opportunity to add new capabilities such as branched mechanisms, collision checking, URDF import, and interfaces to ROS. With familiar, simple yet powerful functions; the clarity of Python syntax; but without the complexity of ROS; users from beginner to advanced will find this a powerful open-source toolset for ongoing robotics education and research.