TacPulse: Pulse Measurement via Vision-Based Tactile Sensing

TacPulse is apulse-sensing system built on vision- based tactile technology. Pulse information is conventionally acquired using optical or electrical techniques, often relying on electrode patches or ultrasound imaging. In contrast, TacPulse employs a bio-inspired, fingertip-sized soft sensor that concentrates pressure over the radial or ulnar artery, while an embedded camera captures skin deformations in real…

UniTac-VAE: A Depth-Aligned Latent Space for Sensor-Agnostic Tactile Representation

Tactile sensing enables great benefits for robotic manipulation, yet the algorithms used to interpret tactile-sensor data often fail to generalize across different sensor types because of incompatible signal formats and distributions. We present UniTac-VAE, a unified tactile representation framework that aligns heterogeneous tactile modalities to a shared, geometry-grounded latent space using local contact depth maps…

Prototype-Driven Diffusion Transformer for Multimode Industrial Soft Sensing

Soft sensors play a crucial role in modern process industries by providing virtual measurements of key quality variables from readily available process data. However, real industrial processes typically exhibit multiple operating conditions, strong nonlinearity, and pronounced time variation, while reliable mode labels are rarely available, making it difficult for conventional deep soft sensors to maintain…

CAVERS: Multimodal SLAM Data From a Natural Karstic Cave With Ground Truth Motion Capture

Autonomous robots operating in natural karstic caves face perception and navigation challenges that are qualitatively distinct from those encountered in mines or tunnels: irregular geometry, reflective wet surfaces, near-zero ambient light, and complex branching passages. Yet publicly available datasets targeting this environment remain scarce and offer limited sensing modalities and environmental diversity. We present CAVERS,…

LSTP-Nav: Lightweight Spatiotemporal Policy for Map-Free Multi-Agent Navigation With LiDAR

Safe map-free multi-robot navigation requires robots to make real-time decisions from partial and noisy local observations in dynamic, unstructured environments. Existing approaches often depend on prior maps, computationally intensive perception pipelines, or carefully tuned interaction models, which limit their robustness on resource-constrained platforms. This paper proposes LSTP-Nav, a lightweight, decentralized navigation framework built on LSTP-Net…

InLiER: Learning-Free Heterogeneous LiDAR Place Recognition via Intermediate Mixed-Radix Structural Keypoint Tokenization

LiDAR place recognition supports loop closure, relocalization, and multi-agent map management. As robotic platforms increasingly combine LiDARs with different fields of view, resolutions, and scanning patterns, existing descriptors degrade because they are tightly coupled to sensor-specific characteristics. We present InLiER, a learning-free pipeline based on an intermediate tokenization step. Height-sliced keypoints from structural elements receive…

Force-Sensorless Acceleration-Based Impedance Control for the Inertia Compensation in the Assisted Manipulation of Large Payloads

In physical human-robot interaction applications involving the co-manipulation of heavy and bulky payloads, inertia compensation of the load is a key element for the ergonomics, precision and comfort of the task led by a human operator. This letter investigates experimentally the capability and limitations of a force-sensorless, acceleration-based impedance control law to render a reduced…

Tether-Inertial Localization for Planetary Drones

Recent developments in planetary exploration have shown the potential of Uncrewed Aerial Vehicles (UAVs), such as the Ingenuity helicopter that provided valuable mapping data. However, limited payload capabilities constrain the flight times and compute available for localization which restrict their applicability. By providing a tethered connection, issues such as battery and computational constraints are offloaded…

Sparse Adaptive Kernel Kalman Filter for Nonlinear Non-Gaussian State Estimation

The adaptive kernel Kalman filter (AKKF) provides a state estimation framework for nonlinear and non-Gaussian systems by synergizing data-space particle propagation with kernel-space Kalman updates. However, expanding the particle set to improve tracking accuracy inevitably induces severe computational burden and numerical ill-conditioning due to dense Gram matrix operations. To tackle that, this paper proposes a…

PointDiffuse: A Dual-Conditional Diffusion Model for Enhanced Point Cloud Semantic Segmentation

Diffusion probabilistic models are traditionally used to generate colors at fixed pixel positions in 2D images. Building on this, we extend diffusion models to point cloud semantic segmentation, where point positions also remain fixed, and the diffusion model generates point labels instead of colors. To accelerate the denoising process in reverse diffusion, we introduce a…