Abstract: The rapid aging of societies is intensifying demand for autonomous care robots; however, most existing systems are task - specific and rely on handcrafted preprocessing, limiting their ability to generalize across diverse scenarios. A prevailing theory in cognitive neuroscience proposes that the human brain operates through hierarchical predictive processing, which underlies flexible cognition and behavior by integrating multimodal sensory signals . Inspired by this principle, we introduce a hierarchical multimodal recurrent neural network grounded in predictive processing under the free - energy principle, capable of directly integrating over 30,000 - dimensional visuo - proprioceptive inputs without dimensionality reduction. The model was able to learn two representative caregiving tasks, rigid - body repositioning and flexible - towel wiping, without task - specific feature engineering. We demonstrate three key proper ties: (i) self - organization of hierarchical latent dynamics that regulate task transitions, capture variability in uncertainty, and infer occluded states; (ii) robustness to degraded vision through visuo - proprioceptive integration; and (iii) asymmetric interference in multitask learning, where the more variable wiping task had little influence on repositioning, whereas learning the repositioning task led to a modest reduction in wiping performance, while the model maintained overall robustness . Although the evaluat ion was limit ed to simulation, these results establish predictive processing as a universal and scalable computational principle, pointing toward robust, flexible, and autonomous care giving robots while offering theoretical insight into the human brain's ability to achieve flexible adaptation in uncertain real - world environments. Main Text: INTRODUCTION As societies worldwide age rapid ly, the growing demand for long - term care is exacerbated by an increasingly severe shortage of professional caregivers ( 1 - 3) . Physically demanding tasks such as patient repositioning or body cleaning are not only labor intensive but also a leading cause of musculoskeletal disorders, particularly lower - back pain, among caregivers ( 4, 5) . To address these challenges, various assistive robotic technologies have been developed ( 6 - 12), ranging from transfer devices and exoskeletons to humanoid systems designed for lifting or repositioning patients. However, most existing systems are either intended to support human operators or are specialized for a single, narrowly defined task, thus limiting their utility across the diverse and unpredictable scenarios encountered in real care settings.

Echocardiography is a key imaging modality for cardiac assessment but remains highly operator-dependent, and access to trained sonographers is limited in underserved settings. Teleoperated robotic echocardiography has been proposed as a solution; however, clinical studies report longer examination times than manual procedures, increasing diagnostic delays and operator workload. Automating non-expert tasks, such as automatically moving the probe to an ideal starting pose, offers a pathway to reduce this burden. Prior vision- and depth-based approaches to estimate an initial probe pose are sensitive to lighting, texture, and anatomical variability. We propose a robot-mounted 2D LiDAR-based approach that reconstructs the chest surface in 3D and estimates the initial probe pose automatically. To the best of our knowledge, this is the first demonstration of robot-mounted 2D LiDAR used for 3D reconstruction of a human body surface. Through plane-based extrinsic calibration, the transformation between the LiDAR and robot base frames was estimated with an overall root mean square (RMS) residual of 1.8 mm and rotational uncertainty below 0.2°. The chest front surface, reconstructed from two linear LiDAR sweeps, was aligned with non-rigid templates to identify an initial probe pose. A mannequin-based study assessing reconstruction accuracy showed mean surface errors of 2.78 +/- 0.21 mm. Human trials (N=5) evaluating the proposed approach found probe initial points typically 20-30 mm from the clinically defined initial point, while the variation across repeated trials on the same subject was less than 4 mm.

Augmented Reality (AR) systems, while enhancing task performance through real-time guidance, pose risks of inducing cognitive tunneling-a hyperfocus on virtual content that compromises situational awareness (SA) in safety-critical scenarios. This paper investigates SA in AR-guided cardiopulmonary resuscitation (CPR), where responders must balance effective compressions with vigilance to unpredictable hazards (e.g., patient vomiting). We developed an AR app on a Magic Leap 2 that overlays real-time CPR feedback (compression depth and rate) and conducted a user study with simulated unexpected incidents (e.g., bleeding) to evaluate SA, in which SA metrics were collected via observation and questionnaires administered during freeze-probe events. Eye tracking analysis revealed that higher SA levels were associated with greater saccadic amplitude and velocity, and with reduced proportion and frequency of fixations on virtual content. To predict SA, we propose FixGraphPool, a graph neural network that structures gaze events (fixations, saccades) into spatiotemporal graphs, effectively capturing dynamic attentional patterns. Our model achieved 83.0% accuracy (F1=81.0%), outperforming feature-based machine learning and state-of-the-art time-series models by leveraging domain knowledge and spatial-temporal information encoded in ET data. These findings demonstrate the potential of eye tracking for SA modeling in AR and highlight its utility in designing AR systems that ensure user safety and situational awareness.

On July 21 at 9 am PT, Anthropic retired Claude 3 Sonnet, a lightweight model known for being quick and cost-effective. On Saturday, in a large warehouse in San Francisco's SOMA district, more than 200 people gathered to mourn its passing. The star-studded funeral was put on by a group of Claude fanatics and Gen Z founders, one of whom told me he dropped out of college after learning about artificial general intelligence. Attendees included Amanda Askell, an Anthropic researcher who has jokingly called herself the "Fairy Claudemother," staffers from Anthropic and OpenAI, and high-profile X posters including the writer Noah Smith. The warehouse was dimly lit, with a tentacle from a shoggoth (a fictional H.P. Lovecraft creature that's become a popular metaphor for AI models) hanging from the ceiling.

-- Tracking the motion of humans or objects in a robot's surroundings is essential to improve safe robot motions and reactions. In this work, we present an approach for scene flow estimation from low-density and noisy point clouds acquired from miniaturised Time-of-Flight (T oF) sensors distributed across the robot's body. The proposed method clusters points from consecutive frames and applies the Iterative Closest Point (ICP) algorithm to estimate a dense motion flow, with additional steps introduced to mitigate the impact of sensor noise and low-density data points. Specifically, we employ a fitness-based classification to distinguish between stationary and moving points and an inlier removal strategy to refine geometric correspondences. The proposed approach is validated in an experimental setup where 24 T oF are used to estimate the velocity of an object moving at different controlled speeds. Experimental results show that the method consistently approximates the direction of the motion and its magnitude with an error which is in line with sensor noise. Robots operating in cluttered or shared environments must be aware of their surroundings to plan safe motions effectively. Tracking the motion of nearby humans and obstacles is crucial for detecting and reacting to potential collisions, as well as improving human-robot collaboration [1]-[4].

This work introduces a robotic dummy head that fuses the acoustic realism of conventional audiological mannequins with the mobility of robots. The proposed device is capable of moving, talking, and listening as people do, and can be used to automate spatially-stationary audio experiments, thus accelerating the pace of audio research. Critically, the device may also be used as a moving sound source in dynamic experiments, due to its quiet motor. This feature differentiates our work from previous robotic acoustic research platforms. Validation that the robot enables high quality audio data collection is provided through various experiments and acoustic measurements. These experiments also demonstrate how the robot might be used to study adaptive binaural beamforming. Design files are provided as open-source to stimulate novel audio research.

A versatile robot working in a domestic environment based on a deep neural network (DNN) is currently attracting attention. One of the roles expected for domestic robots is caregiving for a human. In particular, we focus on repositioning care because repositioning plays a fundamental role in supporting the health and quality of life of individuals with limited mobility. However, generating motions of the repositioning care, avoiding applying force to non-target parts and applying appropriate force to target parts, remains challenging. In this study, we proposed a DNN-based architecture using visual and somatosensory attention mechanisms that can generate dual-arm repositioning motions which involve different sequential policies of interaction force; contact-less reaching and contact-based assisting motions. We used the humanoid robot Dry-AIREC, which features the capability to adjust joint impedance dynamically. In the experiment, the repositioning assistance from the supine position to the sitting position was conducted by Dry-AIREC. The trained model, utilizing the proposed architecture, successfully guided the robot's hand to the back of the mannequin without excessive contact force on the mannequin and provided adequate support and appropriate contact for postural adjustment.

For the shape control of deformable free-form surfaces, simulation plays a crucial role in establishing the mapping between the actuation parameters and the deformed shapes. The differentiation of this forward kinematic mapping is usually employed to solve the inverse kinematic problem for determining the actuation parameters that can realize a target shape. However, the free-form surfaces obtained from simulators are always different from the physically deformed shapes due to the errors introduced by hardware and the simplification adopted in physical simulation. To fill the gap, we propose a novel deformation function based sim-to-real learning method that can map the geometric shape of a simulated model into its corresponding shape of the physical model. Unlike the existing sim-to-real learning methods that rely on completely acquired dense markers, our method accommodates sparsely distributed markers and can resiliently use all captured frames -- even for those in the presence of missing markers. To demonstrate its effectiveness, our sim-to-real method has been integrated into a neural network-based computational pipeline designed to tackle the inverse kinematic problem on a pneumatically actuated deformable mannequin.

As a child-size mannequin stands in a traffic lane on a rural two-lane road, a Tesla in Full Self-Driving mode barrels toward it. And the car drives on, as if nothing happened. It's the latest salvo from activist organization the Dawn Project, which publishes videos aimed at showing how badly Tesla's automated driving technology can behave. Dan O'Dowd, the wealthy, tech-savvy activist who founded and self-funds the Dawn Project, said he wants to ensure that "the safety-critical systems that everyone's life depends on are fail-safe and can't be hacked." While O'Dowd's stated goal is brand-agnostic, his main target since launching the Dawn Project in 2021 has been Tesla and its controversial Autopilot and Full Self-Driving systems.

Soft robotics hold promise in the development of safe yet powered assistive wearable devices for infants. Key to this is the development of closed-loop controllers that can help regulate pneumatic pressure in the device's actuators in an effort to induce controlled motion at the user's limbs and be able to track different types of trajectories. This work develops a controller for soft pneumatic actuators aimed to power a pediatric soft wearable robotic device prototype for upper extremity motion assistance. The controller tracks desired trajectories for a system of soft pneumatic actuators supporting two-degree-of-freedom shoulder joint motion on an infant-sized engineered mannequin. The degrees of freedom assisted by the actuators are equivalent to shoulder motion (abduction/adduction and flexion/extension). Embedded inertial measurement unit sensors provide real-time joint feedback. Experimental data from performing reaching tasks using the engineered mannequin are obtained and compared against ground truth to evaluate the performance of the developed controller. Results reveal the proposed controller leads to accurate trajectory tracking performance across a variety of shoulder joint motions.