Acoustophoresis uses sound waves to manipulate small objects in mid-air and has broad potential in various applications. However, stable multi-particle levitation remains challenging due to complex acoustic dynamics and limitations of existing models. We introduce AcoustoReinforce, a reinforcement learning-based path planner that autonomously controls the motion of multiple levitated particles. Leveraging a decentralized architecture, it learns local neural policies that generate particle trajectories independently, enabling scalable, communication-free control even in densely populated acoustic fields. To ensure physical feasibility, acoustic trapping strength is incorporated as a constraint during both training and inference, producing trajectories that are collision-free, acoustically stable, and physically realizable within real-world system constraints. Experiments on a real-world levitation platform show that AcoustoReinforce outperforms state-of-the-art planners, improving task success rates by up to 130% across diverse configurations. These results demonstrate the effectiveness of learning-based decentralized control for complex multi-object acoustophoresis in real environments.

Acoustic levitation is an emerging technique that has found application in contactless assembly and dynamic displays. It uses precise phase control in an ultrasound transducer array to manage the positions and movements of multiple particles. Yet, maintaining stable mid-air particles is challenging, with unexpected drops disrupting the intended motion and position. Here, we present StableLev, a data-driven pipeline for the detection and amendment of instabilities in multi-particle levitation. We first curate a hybrid levitation dataset, blending optimized simulations with labels based on actual trajectory outcomes. We then design an AutoEncoder to detect anomalies in the simulated data, correlating closely with observed particle drops. Finally, we reconstruct the acoustic field at anomaly regions to improve particle stability and experimentally demonstrate successful dynamic levitation for trajectories within our dataset. Our work provides new insights into multi-particle levitation and enhances its robustness, which will be valuable in a wide range of applications.

Data physicalisation is a technique that encodes data through the geometric and material properties of an artefact, allowing users to engage with data in a more immersive and multi-sensory way. However, current methods of data physicalisation are limited in terms of their reconfigurability and the types of materials that can be used. Acoustophoresis—a method of suspending and manipulating materials using sound waves—offers a promising solution to these challenges. In this paper, we present DataLev, a design space and platform for creating reconfigurable, multimodal data physicalisations with enriched materiality using acoustophoresis. We demonstrate the capabilities of DataLev through eight examples and evaluate its performance in terms of reconfigurability and materiality. Our work offers a new approach to data physicalisation, enabling designers to create more dynamic, engaging, and expressive artefacts.

Phased arrays of transducers have been quickly evolving in terms of software and hardware with applications in haptics (acoustic vibrations), display (levitation), and audio. Most recently, Multimodal Particle-based Displays (MPDs) have even demonstrated volumetric content that can be seen, heard, and felt simultaneously, without additional instrumentation. However, current software tools only support individual modalities and they do not address the integration and exploitation of the multi-modal potential of MPDs. This is because there is no standardized presentation pipeline tackling the challenges related to presenting such kind of multi-modal content (e.g., multi-modal support, multi-rate synchronization at 10 KHz, visual rendering or synchronization and continuity). This article presents OpenMPD, a low-level presentation engine that deals with these challenges and allows structured exploitation of any type of MPD content (i.e., visual, tactile, audio). We characterize OpenMPD’s performance and illustrate how it can be integrated into higher-level development tools (i.e., Unity game engine). We then illustrate its ability to enable novel presentation capabilities, such as support of multiple MPD contents, dexterous manipulations of fast-moving particles, or novel swept-volume MPD content.

Phased Arrays of Transducers (PATs) allow accurate control of ultrasound fields, with applications in haptics, levitation (i.e. displays) and parametric audio. However, algorithms for multi-point levitation or tactile feedback are usually limited to computing solutions in the order of hundreds of sound-fields per second, preventing the use of multiple high-speed points, a feature that can broaden the scope of applications of PATs. We present GS-PAT , a GPU multi-point phase retrieval algorithm, capable of computing 17K solutions per second for up to 32 simultaneous points in a mid-end consumer grade GPU (NVidia GTX 1660). We describe the algorithm and compare it to state of the art multi-point algorithms used for ultrasound haptics and levitation, showing similar quality of the generated sound-fields, and much higher computation rates. We then illustrate how the shift in paradigm enabled by GS-PAT (i.e. real-time control of several high-speed points) opens new applications for PAT technologies, such as in volumetric fully coloured displays, multi-point spatio-temporal tactile feedback, parametric audio and simultaneous combinations of these modalities.