Palpation, the use of touch in medical examination, is almost exclusively performed by humans. We investigate a proof of concept for an artificial palpation method based on self-supervised learning. Our key idea is that an encoder-decoder framework can learn a representation from a sequence of tactile measurements that contains all the relevant information about the palpated object. We conjecture that such a representation can be used for downstream tasks such as tactile imaging and change detection. With enough training data, it should capture intricate patterns in the tactile measurements that go beyond a simple map of forces - the current state of the art. To validate our approach, we both develop a simulation environment and collect a real-world dataset of soft objects and corresponding ground truth images obtained by magnetic resonance imaging (MRI). We collect palpation sequences using a robot equipped with a tactile sensor, and train a model that predicts sensory readings at different positions on the object. We investigate the representation learned in this process, and demonstrate its use in imaging and change detection.

Tactile perception is essential for embodied agents to understand physical attributes of objects that cannot be determined through visual inspection alone. While existing approaches have made progress in visual and language modalities for physical understanding, they fail to effectively incorporate tactile information that provides crucial haptic feedback for real-world interaction.

Recent cooperative perception datasets have played a crucial role in advancing smart mobility applications by enabling information exchange between intelligent agents, helping to overcome challenges such as occlusions and improving overall scene understanding. While some existing real-world datasets incorporate both vehicle-to-vehicle and vehicle-to-infrastructure interactions, they are typically limited to a single intersection or a single vehicle. A comprehensive perception dataset featuring multiple connected vehicles and infrastructure sensors across several intersections remains unavailable, limiting the benchmarking of algorithms in diverse traffic environments. Consequently, overfitting can occur, and models may demonstrate misleadingly high performance due to similar intersection layouts and traffic participant behavior. To address this gap, we introduce UrbanIng-V2X, the first large-scale, multi-modal dataset supporting cooperative perception involving vehicles and infrastructure sensors deployed across three urban intersections in Ingolstadt, Germany. UrbanIng-V2X consists of 34 temporally aligned and spatially calibrated sensor sequences, each lasting 20 seconds. All sequences contain recordings from one of three intersections, involving two vehicles and up to three infrastructure-mounted sensor poles operating in coordinated scenarios. In total, UrbanIng-V2X provides data from 12 vehicle-mounted RGB cameras, 2 vehicle LiDARs, 17 infrastructure thermal cameras, and 12 infrastructure LiDARs. All sequences are annotated at a frequency of 10 Hz with 3D bounding boxes spanning 13 object classes, resulting in approximately 712k annotated instances across the dataset.

A.1 Coordinate Systems and Transformation To achieve spatial synchronization between different sensors, vehicle-vehicle-UAV collaboration requires using sensor parameter information to perform coordinate system transformations. The relationships between the coordinate systems are illustrated in Fig. S 1. Figure 1: Relationship between coordinate systems. The pixel coordinate system refers to a two-dimensional coordinate system defined on the image plane, typically represented as (u,v), with units in pixels. In this system, the origin is located at the top-left corner of the image, the u-axis points to the right along the horizontal direction, and the v-axis points downward along the vertical direction. This coordinate system is used to describe the position of points on the two-dimensional image captured by the camera.

Comparison of RAPID, LEAP, and Allegro infinger thickness, MCP joint configurations, and dexterity, illustrated with representative finger poses.

As a bio-inspired vision sensor, spike camera records light intensity by accumulating photons and firing a spike once a preset threshold is reached. For high-light regions, the accumulated photons may reach the threshold multiple times within a readout interval, while only one spike can be stored and read out, resulting in incorrect intensity representation and a limited dynamic range. Multi-level (ML) spike camera enhances the dynamic range by introducing a spike-firing counter (SFC) to count spikes within each readout interval for each pixel, and uses different spike symbols to represent the arrival of different amounts of photons. However, when the light intensity becomes even higher, each pixel requires an SFC with a higher bit depth, causing great cost to the manufacturing process. To address these issues, we propose time-encoding (TE) spike camera, which transforms the counting of spikes to recording of the time at which a specific number of spikes (i.e., an overflow) is reached.

Sensor fusion of camera, LiDAR, and 4-dimensional (4D) Radar has brought a significant performance improvement in autonomous driving. However, there still exist fundamental challenges: deeply coupled fusion methods assume continuous sensor availability, making them vulnerable to sensor degradation and failure, whereas sensor-wise cross-attention fusion methods struggle with computational cost and unified feature representation. This paper presents availability-aware sensor fusion (ASF), a novel method that employs unified canonical projection (UCP) to enable consistency in all sensor features for fusion and cross-attention across sensors along patches (CASAP) to enhance robustness of sensor fusion against sensor degradation and failure. As a result, the proposed ASF shows a superior object detection performance to the existing state-of-the-art fusion methods under various weather and sensor degradation (or failure) conditions. Extensive experiments on the K-Radar dataset demonstrate that ASF achieves improvements of 9.7% in APBEV (87.2%) and 20.1% in AP3D (73.6%) in object detection at IoU=0.5, while requiring a low computational cost.

Sparse inertial measurement units (IMUs) provide a portable, low-cost solution for human motion tracking but struggle with error accumulation from drift and sensor noise when estimating joint position through time-based linear acceleration integration (i.e., indirect measurement). To address this, we propose ToF-IP, a novel 3D full-body pose estimation system that integrates Time-of-Flight (ToF) sensors with sparse IMUs. The distinct advantage of our approach is that ToF sensors provide direct distance measurements, effectively mitigating error accumulation without relying on indirect time-based integration. From a hardware perspective, we maintain the portability of existing solutions by attaching ToF sensors to selected IMUs with a negligible volume increase of just 3%. On the software side, we introduce two novel techniques to enhance multi-sensor integration: (i) a NodeCentric Data Integration strategy that leverages a Transformer encoder to explicitly model both intra-node and inter-node data integration by treating each sensing node as a token; and (ii) a Dynamic Spatial Positional Encoding scheme that encodes the continuously changing spatial positions of wearable nodes as motion-conditioned functions, enabling the model to better capture human body dynamics in the embedding space. Additionally, we contribute a 208-minute human motion dataset from 10 participants, including synchronized IMU-ToF measurements and groundtruth from optical tracking. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches such as PNP, achieving superior accuracy in tracking complex and slow motions like Tai Chi, which remains challenging for inertial-only methods.

But you have to eat it. The sensor developed at MIT continuously monitors this vital sign from inside the body. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The silicon chip, the battery, and the antenna on this sensor are completely ingestible. Breakthroughs, discoveries, and DIY tips sent six days a week.

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