Abstract--We introduce a method for manipulating objects in three-dimensional space using controlled fluid streams. To achieve this, we train a neural network controller in a differentiable simulation and evaluate it in a simulated environment consisting of an 8 8 grid of vertical emitters. By carrying out various horizontal displacement tasks such as moving objects to specific positions while reacting to external perturbations, we demonstrate that a controller, trained with a limited number of iterations, can generalise to longer episodes and learn the complex dynamics of fluid-solid interactions. Importantly, our approach requires only the observation of the manipulated object's state, paving the way for the development of physical systems that enable contactless manipulation of objects using air streams. Real-time control of systems involving fluid and solid interactions presents inherent challenges due to the complex dynamics arising from their interactions and the highdimensional state space.

Augmenting automated vehicles to wirelessly detect and respond to external events before they are detectable by onboard sensors is crucial for developing context-aware driving strategies. To this end, we present an automated vehicle platform, designed with connectivity, ease of use and modularity in mind, both in hardware and software. It is based on the Kia Soul EV with a modified version of the Open-Source Car Control (OSCC) drive-by-wire module, uses the open-source Robot Operating System (ROS and ROS 2) in its software architecture, and provides a straightforward solution for transitioning from simulations to real-world tests. We demonstrate the effectiveness of the platform through a synchronised driving test, where sensor data is exchanged wirelessly, and a model-predictive controller is used to actuate the automated vehicle.