Contact Compliance Visuo-Proprioceptive Policy for Contact-Rich Manipulation with Cost-Efficient Haptic Hand-Arm Teleoperation System

Learning robot manipulation skills in real-world environments is extremely challenging. Robots learning manipulation skills in real-world environments is extremely challenging. Recent research on imitation learning and visuomotor policies has significantly enhanced the ability of robots to perform manipulation tasks. In this paper, we propose Admit Policy, a visuo-proprioceptive imitation learning framework with force compliance, designed to reduce contact force fluctuations during robot execution of contact-rich manipulation tasks. This framework also includes a hand-arm teleoperation system with vibrotactile feedback for efficient data collection. Our framework utilizes RGB images, robot joint positions, and contact forces as observations and leverages a consistency-constrained teacher-student probabilistic diffusion model to generate future trajectories for end-effector positions and contact forces. An admittance model is then employed to track these trajectories, enabling effective force-position control across various tasks.We validated our framework on five challenging contact-rich manipulation tasks. Among these tasks, while improving success rates, our approach most significantly reduced the mean contact force required to complete the tasks by up to 53.92% and decreased the standard deviation of contact force fluctuations by 76.51% compared to imitation learning algorithms without dynamic contact force prediction and tracking.

Noise-aware Dynamic Image Denoising and Positron Range Correction for Rubidium-82 Cardiac PET Imaging via Self-supervision

Rb-82 is a radioactive isotope widely used for cardiac PET imaging. Despite numerous benefits of 82-Rb, there are several factors that limits its image quality and quantitative accuracy. First, the short half-life of 82-Rb results in noisy dynamic frames. Low signal-to-noise ratio would result in inaccurate and biased image quantification. Noisy dynamic frames also lead to highly noisy parametric images. The noise levels also vary substantially in different dynamic frames due to radiotracer decay and short half-life. Existing denoising methods are not applicable for this task due to the lack of paired training inputs/labels and inability to generalize across varying noise levels. Second, 82-Rb emits high-energy positrons. Compared with other tracers such as 18-F, 82-Rb travels a longer distance before annihilation, which negatively affect image spatial resolution. Here, the goal of this study is to propose a self-supervised method for simultaneous (1) noise-aware dynamic image denoising and (2) positron range correction for 82-Rb cardiac PET imaging. Tested on a series of PET scans from a cohort of normal volunteers, the proposed method produced images with superior visual quality. To demonstrate the improvement in image quantification, we compared image-derived input functions (IDIFs) with arterial input functions (AIFs) from continuous arterial blood samples. The IDIF derived from the proposed method led to lower AUC differences, decreasing from 11.09% to 7.58% on average, compared to the original dynamic frames. The proposed method also improved the quantification of myocardium blood flow (MBF), as validated against 15-O-water scans, with mean MBF differences decreased from 0.43 to 0.09, compared to the original dynamic frames. We also conducted a generalizability experiment on 37 patient scans obtained from a different country using a different scanner.

Towards certifiable AI in aviation: landscape, challenges, and opportunities

Artificial Intelligence (AI) methods are powerful tools for various domains, including critical fields such as avionics, where certification is required to achieve and maintain an acceptable level of safety. General solutions for safety-critical systems must address three main questions: Is it suitable? What drives the system's decisions? Is it robust to errors/attacks? This is more complex in AI than in traditional methods. In this context, this paper presents a comprehensive mind map of formal AI certification in avionics. It highlights the challenges of certifying AI development with an example to emphasize the need for qualification beyond performance metrics.

A Wearable Multi-Modal Edge-Computing System for Real-Time Kitchen Activity Recognition

In the human activity recognition research area, prior studies predominantly concentrate on leveraging advanced algorithms on public datasets to enhance recognition performance, little attention has been paid to executing real-time kitchen activity recognition on energy-efficient, cost-effective edge devices. Besides, the prevalent approach of segregating data collection and context extraction across different devices escalates power usage, latency, and user privacy risks, impeding widespread adoption. This work presents a multi-modal wearable edge computing system for human activity recognition in real-time. Integrating six different sensors, ranging from inertial measurement units (IMUs) to thermal cameras, and two different microcontrollers, this system achieves end-to-end activity recognition, from data capture to context extraction, locally. Evaluation in an unmodified realistic kitchen validates its efficacy in recognizing fifteen activities, including a null class. Employing a compact machine learning model (184.5 kbytes) yields an average accuracy of 87.83 \%, with model inference completed in 25.26 ms on the microcontroller. Comparative analysis with alternative microcontrollers showcases power consumption and inference speed performance, demonstrating the proposed system's viability.

TSAK: Two-Stage Semantic-Aware Knowledge Distillation for Efficient Wearable Modality and Model Optimization in Manufacturing Lines

Smaller machine learning models, with less complex architectures and sensor inputs, can benefit wearable sensor-based human activity recognition (HAR) systems in many ways, from complexity and cost to battery life. In the specific case of smart factories, optimizing human-robot collaboration hinges on the implementation of cutting-edge, human-centric AI systems. To this end, workers' activity recognition enables accurate quantification of performance metrics, improving efficiency holistically. We present a two-stage semantic-aware knowledge distillation (KD) approach, TSAK, for efficient, privacy-aware, and wearable HAR in manufacturing lines, which reduces the input sensor modalities as well as the machine learning model size, while reaching similar recognition performance as a larger multi-modal and multi-positional teacher model. The first stage incorporates a teacher classifier model encoding attention, causal, and combined representations. The second stage encompasses a semantic classifier merging the three representations from the first stage. To evaluate TSAK, we recorded a multi-modal dataset at a smart factory testbed with wearable and privacy-aware sensors (IMU and capacitive) located on both workers' hands. In addition, we evaluated our approach on OpenPack, the only available open dataset mimicking the wearable sensor placements on both hands in the manufacturing HAR scenario. We compared several KD strategies with different representations to regulate the training process of a smaller student model. Compared to the larger teacher model, the student model takes fewer sensor channels from a single hand, has 79% fewer parameters, runs 8.88 times faster, and requires 96.6% less computing power (FLOPS).

ALS-HAR: Harnessing Wearable Ambient Light Sensors to Enhance IMU-based Human Activity Recogntion

Despite the widespread integration of ambient light sensors (ALS) in smart devices commonly used for screen brightness adaptation, their application in human activity recognition (HAR), primarily through body-worn ALS, is largely unexplored. In this work, we developed ALS-HAR, a robust wearable light-based motion activity classifier. Although ALS-HAR achieves comparable accuracy to other modalities, its natural sensitivity to external disturbances, such as changes in ambient light, weather conditions, or indoor lighting, makes it challenging for daily use. To address such drawbacks, we introduce strategies to enhance environment-invariant IMU-based activity classifications through augmented multi-modal and contrastive classifications by transferring the knowledge extracted from the ALS. Our experiments on a real-world activity dataset for three different scenarios demonstrate that while ALS-HAR's accuracy strongly relies on external lighting conditions, cross-modal information can still improve other HAR systems, such as IMU-based classifiers.Even in scenarios where ALS performs insufficiently, the additional knowledge enables improved accuracy and macro F1 score by up to 4.2 % and 6.4 %, respectively, for IMU-based classifiers and even surpasses multi-modal sensor fusion models in two of our three experiment scenarios. Our research highlights the untapped potential of ALS integration in advancing sensor-based HAR technology, paving the way for practical and efficient wearable ALS-based activity recognition systems with potential applications in healthcare, sports monitoring, and smart indoor environments.

Spectrum Prediction With Deep 3D Pyramid Vision Transformer Learning

In this paper, we propose a deep learning (DL)-based task-driven spectrum prediction framework, named DeepSPred. The DeepSPred comprises a feature encoder and a task predictor, where the encoder extracts spectrum usage pattern features, and the predictor configures different networks according to the task requirements to predict future spectrum. Based on the Deep- SPred, we first propose a novel 3D spectrum prediction method combining a flow processing strategy with 3D vision Transformer (ViT, i.e., Swin) and a pyramid to serve possible applications such as spectrum monitoring task, named 3D-SwinSTB. 3D-SwinSTB unique 3D Patch Merging ViT-to-3D ViT Patch Expanding and pyramid designs help the model accurately learn the potential correlation of the evolution of the spectrogram over time. Then, we propose a novel spectrum occupancy rate (SOR) method by redesigning a predictor consisting exclusively of 3D convolutional and linear layers to serve possible applications such as dynamic spectrum access (DSA) task, named 3D-SwinLinear. Unlike the 3D-SwinSTB output spectrogram, 3D-SwinLinear projects the spectrogram directly as the SOR. Finally, we employ transfer learning (TL) to ensure the applicability of our two methods to diverse spectrum services. The results show that our 3D-SwinSTB outperforms recent benchmarks by more than 5%, while our 3D-SwinLinear achieves a 90% accuracy, with a performance improvement exceeding 10%.

Misinforming LLMs: vulnerabilities, challenges and opportunities

Large Language Models (LLMs) have made significant advances in natural language processing, but their underlying mechanisms are often misunderstood. Despite exhibiting coherent answers and apparent reasoning behaviors, LLMs rely on statistical patterns in word embeddings rather than true cognitive processes. This leads to vulnerabilities such as "hallucination" and misinformation. The paper argues that current LLM architectures are inherently untrustworthy due to their reliance on correlations of sequential patterns of word embedding vectors. However, ongoing research into combining generative transformer-based models with fact bases and logic programming languages may lead to the development of trustworthy LLMs capable of generating statements based on given truth and explaining their self-reasoning process.

ATI-CTLO:Adaptive Temporal Interval-based Continuous-Time LiDAR-Only Odometry

The motion distortion in LiDAR scans caused by aggressive robot motion and varying terrain features significantly impacts the positioning and mapping performance of 3D LiDAR odometry. Existing distortion correction solutions often struggle to balance computational complexity and accuracy. In this work, we propose an Adaptive Temporal Interval-based Continuous-Time LiDAR-only Odometry, utilizing straightforward and efficient linear interpolation. Our method flexibly adjusts the temporal intervals between control nodes according to the dynamics of motion and environmental characteristics. This adaptability enhances performance across various motion states and improves robustness in challenging, feature-sparse environments. We validate the effectiveness of our method on multiple datasets across different platforms, achieving accuracy comparable to state-of-the-art LiDAR-only odometry methods. Notably, in scenarios involving aggressive motion and sparse features, our method outperforms existing solutions.

Initial Investigation of Kolmogorov-Arnold Networks (KANs) as Feature Extractors for IMU Based Human Activity Recognition

In this work, we explore the use of a novel neural network architecture, the Kolmogorov-Arnold Networks (KANs) as feature extractors for sensor-based (specifically IMU) Human Activity Recognition (HAR). Where conventional networks perform a parameterized weighted sum of the inputs at each node and then feed the result into a statically defined nonlinearity, KANs perform non-linear computations represented by B-SPLINES on the edges leading to each node and then just sum up the inputs at the node. Instead of learning weights, the system learns the spline parameters. In the original work, such networks have been shown to be able to more efficiently and exactly learn sophisticated real valued functions e.g. in regression or PDE solution. We hypothesize that such an ability is also advantageous for computing low-level features for IMU-based HAR. To this end, we have implemented KAN as the feature extraction architecture for IMU-based human activity recognition tasks, including four architecture variations. We present an initial performance investigation of the KAN feature extractor on four public HAR datasets. It shows that the KAN-based feature extractor outperforms CNN-based extractors on all datasets while being more parameter efficient.