A Gesture-Based Visual Learning Model for Acoustophoretic Interactions using a Swarm of AcoustoBots
- URL: http://arxiv.org/abs/2604.19643v1
- Date: Tue, 21 Apr 2026 16:32:38 GMT
- Title: A Gesture-Based Visual Learning Model for Acoustophoretic Interactions using a Swarm of AcoustoBots
- Abstract summary: AcoustoBots are mobile acoustophoretic robots capable of delivering haptics, directional audio, and acoustic levitation.<n>Existing implementations rely on scripted commands and lack an intuitive interface for real-time human control.<n>This work presents a gesture-based visual learning framework for contactless human-swarm interaction with a multimodal AcoustoBot platform.
- Score: 8.171680721155761
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: AcoustoBots are mobile acoustophoretic robots capable of delivering mid-air haptics, directional audio, and acoustic levitation, but existing implementations rely on scripted commands and lack an intuitive interface for real-time human control. This work presents a gesture-based visual learning framework for contactless human-swarm interaction with a multimodal AcoustoBot platform. The system combines ESP32-CAM gesture capture, PhaseSpace motion tracking, centralized processing, and an OpenCLIP-based visual learning model (VLM) with linear probing to classify three hand gestures and map them to haptics, audio, and levitation modalities. Validation accuracy improved from about 67% with a small dataset to nearly 98% with the largest dataset. In integrated experiments with two AcoustoBots, the system achieved an overall gesture-to-modality switching accuracy of 87.8% across 90 trials, with an average end-to-end latency of 3.95 seconds. These results demonstrate the feasibility of using a vision-language-model-based gesture interface for multimodal human-swarm interaction. While the current system is limited by centralized processing, a static gesture set, and controlled-environment evaluation, it establishes a foundation for more expressive, scalable, and accessible swarm robotic interfaces.
Related papers
- An Intelligent-Cloud Edge Multimodal Interaction System for Robots [4.1212219465063304]
This paper presents a cloud-edge multimodal interaction framework that integrates an enhanced YOLO-based gesture detector with coordinated large language model (LLM) and vision-language model (VLM) agents.<n>The cloud layer performs gesture detection, scene understanding, multimodal fusion, and action planning, whereas the TonyPi robot locally handles data acquisition, communication, action execution, and feedback.
arXiv Detail & Related papers (2026-07-16T07:39:18Z) - Real-Time sEMG-Based Telecontrol of an Assistive Robotic Arm Using a 1D Convolutional Neural Network [0.30786914102688595]
This work proposes a real-time sEMG-based interface for the teleoperation of an assistive robotic arm.<n>The system relies on four-channel sEMG acquisition, signal preprocessing, segmentation into sliding windows, and classification using a one-dimensional convolutional neural network (CNN)<n>The system exhibits stable real-time behavior, with an average latency of approximately 0.32 s consistent with the chosen windowing strategy.
arXiv Detail & Related papers (2026-07-14T15:34:28Z) - Embodied Human-Robot Interaction via Acoustics: A MARL Approach with AcoustoBots for Spatial Data Physicalization [32.62028759076795]
AcoustoBots is a mobile acoustophoretic data-physicalization platform.<n>TurtleBot3 robots carry upward-facing 8 x 8 ultrasonic phased arrays.<n>Each array levitates a particle whose height encodes a local urban scalar value, such as population density, noise, or traffic.
arXiv Detail & Related papers (2026-07-07T17:59:44Z) - Robotic Grasping and Placement Controlled by EEG-Based Hybrid Visual and Motor Imagery [64.82869118243723]
We present a framework that integrates EEG-based visual and motor imagery (VI/MI) with robotic control to enable real-time, intention-driven grasping and placement.<n>Motivated by the promise of BCI-driven robotics, this system bridges neural signals with physical control by deploying offline-pretrained decoders in a zero-shot manner.
arXiv Detail & Related papers (2026-03-03T17:41:42Z) - An Approach to Combining Video and Speech with Large Language Models in Human-Robot Interaction [0.0]
This work presents a novel HRI framework that combines advanced vision-language models, speech processing, and fuzzy logic.<n>The proposed system integrates Florence-2 for object detection, Llama 3.1 for natural language understanding, and Whisper for speech recognition.<n> Experimental evaluations conducted on consumer-grade hardware demonstrate a command execution accuracy of 75%.
arXiv Detail & Related papers (2026-02-23T09:05:15Z) - How do Foundation Models Compare to Skeleton-Based Approaches for Gesture Recognition in Human-Robot Interaction? [9.094835948226063]
Gestures enable non-verbal human-robot communication in noisy environments like agile production.<n>Traditional deep learning-based gesture recognition relies on task-specific architectures using images, videos, or skeletal pose estimates as input.<n> Vision Foundation Models (VFMs) and Vision Language Models (VLMs) with their strong generalization abilities offer potential to reduce system complexity.<n>This study investigates adapting such models for dynamic, full-body gesture recognition, comparing V-JEPA (a state-of-the-art VFM), Gemini Flash 2.0 (a multimodal VLM), and HD-GCN (a top-performing skeleton-based
arXiv Detail & Related papers (2025-06-25T19:36:45Z) - DiG-Net: Enhancing Quality of Life through Hyper-Range Dynamic Gesture Recognition in Assistive Robotics [2.625826951636656]
We introduce a novel approach designed specifically for assistive robotics, enabling dynamic gesture recognition at extended distances of up to 30 meters.<n>Our proposed Distance-aware Gesture Network (DiG-Net) effectively combines Depth-Conditioned Deformable Alignment (DADA) blocks with Spatio-Temporal Graph modules.<n>By effectively interpreting gestures from considerable distances, DiG-Net significantly enhances the usability of assistive robots in home healthcare, industrial safety, and remote assistance scenarios.
arXiv Detail & Related papers (2025-05-30T16:47:44Z) - Learning Manipulation by Predicting Interaction [85.57297574510507]
We propose a general pre-training pipeline that learns Manipulation by Predicting the Interaction.
The experimental results demonstrate that MPI exhibits remarkable improvement by 10% to 64% compared with previous state-of-the-art in real-world robot platforms.
arXiv Detail & Related papers (2024-06-01T13:28:31Z) - InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint [67.6297384588837]
We introduce a novel controllable motion generation method, InterControl, to encourage the synthesized motions maintaining the desired distance between joint pairs.
We demonstrate that the distance between joint pairs for human-wise interactions can be generated using an off-the-shelf Large Language Model.
arXiv Detail & Related papers (2023-11-27T14:32:33Z) - Robotic Detection of a Human-Comprehensible Gestural Language for
Underwater Multi-Human-Robot Collaboration [16.823029377470363]
We present a motion-based robotic communication framework that enables non-verbal communication among autonomous underwater vehicles (AUVs) and human divers.
We design a gestural language for AUV-to-A communication which can be easily understood by divers observing the conversation.
To allow As to visually understand a gesture from another AUV, we propose a deep network (RRCommNet) which exploits a self-attention mechanism to learn to recognize each message by extracting discrimi-temporal features.
arXiv Detail & Related papers (2022-07-12T06:04:12Z) - Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual
Imitation Learning [62.83590925557013]
We learn a set of challenging partially-observed manipulation tasks from visual and audio inputs.
Our proposed system learns these tasks by combining offline imitation learning from tele-operated demonstrations and online finetuning.
In a set of simulated tasks, we find that our system benefits from using audio, and that by using online interventions we are able to improve the success rate of offline imitation learning by 20%.
arXiv Detail & Related papers (2022-05-30T04:52:58Z) - A Wireless-Vision Dataset for Privacy Preserving Human Activity
Recognition [53.41825941088989]
A new WiFi-based and video-based neural network (WiNN) is proposed to improve the robustness of activity recognition.
Our results show that WiVi data set satisfies the primary demand and all three branches in the proposed pipeline keep more than $80%$ of activity recognition accuracy.
arXiv Detail & Related papers (2022-05-24T10:49:11Z) - Metric-based multimodal meta-learning for human movement identification
via footstep recognition [3.300376360949452]
We describe a novel metric-based learning approach that introduces a multimodal framework.
We learn general-purpose representations from low multisensory data obtained from omnipresent sensing systems.
Our results employ a metric-based contrastive learning approach for multi-sensor data to mitigate the impact of data scarcity.
arXiv Detail & Related papers (2021-11-15T18:46:14Z) - Relational Graph Learning on Visual and Kinematics Embeddings for
Accurate Gesture Recognition in Robotic Surgery [84.73764603474413]
We propose a novel online approach of multi-modal graph network (i.e., MRG-Net) to dynamically integrate visual and kinematics information.
The effectiveness of our method is demonstrated with state-of-the-art results on the public JIGSAWS dataset.
arXiv Detail & Related papers (2020-11-03T11:00:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.