Accurately identifying the material composition of objects is a critical capability for AI robots powered by large language models (LLMs) to perform context-aware manipulation. Radar technologies offer a promising sensing modality for material recognition task. When combined with deep learning, radar technologies have demonstrated strong potential in identifying the material of various objects. However, existing radar-based solutions are often constrained to closed-set object categories and typically require task-specific data collection to train deep learning models, largely limiting their practical applicability. This raises an important question: Can we leverage the powerful reasoning capabilities of pre-trained LLMs to directly infer material composition from raw radar signals? Answering this question is non-trivial due to the inherent redundancy of radar signals and the fact that pre-trained LLMs have no prior exposure to raw radar data during training. To address this, we introduce LLMaterial, the first study to investigate the feasibility of using LLM to identify materials directly from radar signals. First, we introduce a physics-informed signal processing pipeline that distills high-redundancy radar raw data into a set of compact intermediate parameters that encapsulate the material's intrinsic characteristics. Second, we adopt a retrieval-augmented generation (RAG) strategy to provide the LLM with domain-specific knowledge, enabling it to interpret and reason over the extracted intermediate parameters. Leveraging this integration, the LLM is empowered to perform step-by-step reasoning on the condensed radar features, achieving open-set material recognition directly from raw radar signals. Preliminary results show that LLMaterial can effectively distinguish among a variety of common materials, highlighting its strong potential for real-world material identification applications.

Radar has become an essential sensor for autonomous navigation, especially in challenging environments where camera and LiDAR sensors fail. 4D single-chip millimeter-wave radar systems, in particular, have drawn increasing attention thanks to their ability to provide spatial and Doppler information with low hardware cost and power consumption. However, most single-chip radar systems using traditional signal processing, such as Fast Fourier Transform, suffer from limited spatial resolution in radar detection, significantly limiting the performance of radar-based odometry and Simultaneous Localization and Mapping (SLAM) systems. In this paper, we develop a novel radar signal processing pipeline that integrates spatial domain beamforming techniques, and extend it to 3D Direction of Arrival estimation. Experiments using public datasets are conducted to evaluate and compare the performance of our proposed signal processing pipeline against traditional methodologies. These tests specifically focus on assessing structural precision across diverse scenes and measuring odometry accuracy in different radar odometry systems. This research demonstrates the feasibility of achieving more accurate radar odometry by simply replacing the standard FFT-based processing with the proposed pipeline. The codes are available at GitHub*.

The focus of this paper is a proof of concept, machine learning (ML) pipeline that extracts heart rate from pressure sensor data acquired on low-power edge devices. The ML pipeline consists an upsampler neural network, a signal quality classifier, and a 1D-convolutional neural network optimized for efficient and accurate heart rate estimation. The models were designed so the pipeline was less than 40 kB. Further, a hybrid pipeline consisting of the upsampler and classifier, followed by a peak detection algorithm was developed. The pipelines were deployed on ESP32 edge device and benchmarked against signal processing to determine the energy usage, and inference times. The results indicate that the proposed ML and hybrid pipeline reduces energy and time per inference by 82% and 28% compared to traditional algorithms. The main trade-off for ML pipeline was accuracy, with a mean absolute error (MAE) of 3.28, compared to 2.39 and 1.17 for the hybrid and signal processing pipelines. The ML models thus show promise for deployment in energy and computationally constrained devices. Further, the lower sampling rate and computational requirements for the ML pipeline could enable custom hardware solutions to reduce the cost and energy needs of wearable devices.

When we hear about machine learning - whether it's about machines learning to play Go, or computers generating plausible human language - we often think about deep learning. Lots of unstructured data gets thrown in a complex neural network with billions of parameters, and after a very expensive training stage the model learns the task at hand. But this is not always a desirable approach. One of the most interesting places where we can run machine learning is on embedded or IoT devices. These devices already handle a vast amount of high-resolution sensor data, but often need to send the sensor data to the cloud to get analyzed.