As robots increasingly coexist with humans, they must navigate complex, dynamic environments rich in visual information and implicit social dynamics, like when to yield or move through crowds. Addressing these challenges requires significant advances in vision-based sensing and a deeper understanding of socio-dynamic factors, particularly in tasks like navigation. To facilitate this, robotics researchers need advanced simulation platforms offering dynamic, photorealistic environments with realistic actors. Unfortunately, most existing simulators fall short, prioritizing geometric accuracy over visual fidelity, and employing unrealistic agents with fixed trajectories and low-quality visuals. To overcome these limitations, we developed a simulator that incorporates three essential elements: (1) photorealistic neural rendering of environments, (2) neurally animated human entities with behavior management, and (3) an ego-centric robotic agent providing multi-sensor output. By utilizing advanced neural rendering techniques in a dual-NeRF simulator, our system produces high-fidelity, photorealistic renderings of both environments and human entities. Additionally, it integrates a state-of-the-art Social Force Model to model dynamic human-human and human-robot interactions, creating the first photorealistic and accessible human-robot simulation system powered by neural rendering.

Purpose. Proton Magnetic Resonance Spectroscopic Imaging (1H-MRSI) provides non-invasive spectral-spatial mapping of metabolism. However, long-standing problems in whole-brain 1H-MRSI are spectral overlap of metabolite peaks with large lipid signal from scalp, and overwhelming water signal that distorts spectra. Fast and effective methods are needed for high-resolution 1H-MRSI to accurately remove lipid and water signals while preserving the metabolite signal. The potential of supervised neural networks for this task remains unexplored, despite their success for other MRSI processing. Methods. We introduce a deep-learning method based on a modified Y-NET network for water and lipid removal in whole-brain 1H-MRSI. The WALINET (WAter and LIpid neural NETwork) was compared to conventional methods such as the state-of-the-art lipid L2 regularization and Hankel-Lanczos singular value decomposition (HLSVD) water suppression. Methods were evaluated on simulated and in-vivo whole-brain MRSI using NMRSE, SNR, CRLB, and FWHM metrics. Results. WALINET is significantly faster and needs 8s for high-resolution whole-brain MRSI, compared to 42 minutes for conventional HLSVD+L2. Quantitative analysis shows WALINET has better performance than HLSVD+L2: 1) more lipid removal with 41% lower NRMSE, 2) better metabolite signal preservation with 71% lower NRMSE in simulated data, 155% higher SNR and 50% lower CRLB in in-vivo data. Metabolic maps obtained by WALINET in healthy subjects and patients show better gray/white-matter contrast with more visible structural details. Conclusions. WALINET has superior performance for nuisance signal removal and metabolite quantification on whole-brain 1H-MRSI compared to conventional state-of-the-art techniques. This represents a new application of deep-learning for MRSI processing, with potential for automated high-throughput workflow.

The goals of functional Magnetic Resonance Imaging (fMRI) include high spatial and temporal resolutions with a high signal-to-noise ratio (SNR). To simultaneously improve spatial and temporal resolutions and maintain the high SNR advantage of OSSI, we present novel pipelines for fast acquisition and high-resolution fMRI reconstruction and physics parameter quantification. We propose a patch-tensor low-rank model, a physics-based manifold model, and a voxel-wise attention network. With novel models for acquisition and reconstruction, we demonstrate that we can improve SNR and resolution simultaneously without compromising scan time. All the proposed models outperform other comparison approaches with higher resolution and more functional information.

Toronto Pearson is located in Mississauga, west of Toronto, in Ontario, Canada. It is Canada's largest airport and the sixth-most-connected airport in the world. "As the first airport in the world to test HEXWAVE, we see the potential benefits of utilizing this innovative solution as part of our broader airport security program following further testing and evaluation," said Dwayne MacIntosh, Director, Corporate Safety and Security, GTAA. "We were impressed with the HEXWAVE's seamless screening during beta testing and look forward to working with Liberty Defense on the enhanced detection the HEXWAVE would bring to the airport." HEXWAVE uses millimeter wave, advanced 3D imaging, and AI to detect all types of concealed metallic and non-metallic weapons and other prohibited items – without having to divest common items.

Locus Robotics has signed what it claims to be one of the largest deployments of autonomous mobile robots (AMRs) ever. GEODIS is a leading global transport and logistics provider and has used Locus' AMRs since 2018, when it first deployed Locus' AMRs at a site in Indiana. The global third-party logistics company (3PL) has currently deployed Locus AMRs at 14 sites around the world, serving a variety of retail and consumer brands, including warehouses in the U.S and Europe. At press time, Locus hadn't provided how many of its AMRs GEODIS will have overall after these 1,000 are deployed. Locus told The Robot Report it doesn't have concrete evidence this is one of the largest AMR deals ever.

Purpose: To investigate aspects of the validation of self-supervised algorithms for reconstruction of undersampled MR images: quantitative evaluation of prospective reconstructions, potential differences between prospective and retrospective reconstructions, suitability of commonly used quantitative metrics, and generalizability. Theory and Methods: Two self-supervised algorithms based on self-supervised denoising and neural network image priors were investigated. These methods are compared to a least squares fitting and a compressed sensing reconstruction using in-vivo and phantom data. Their generalizability was tested with prospectively under-sampled data from experimental conditions different to the training. Results: Prospective reconstructions can exhibit significant distortion relative to retrospective reconstructions/ground truth. Pixel-wise quantitative metrics may not capture differences in perceptual quality accurately, in contrast to a perceptual metric. All methods showed potential for generalization; generalizability is more affected by changes in anatomy/contrast than other changes. No-reference image metrics correspond well with human rating of image quality for studying generalizability. Compressed Sensing and learned denoising perform similarly well on all data. Conclusion: Self-supervised methods show promising results for accelerating image reconstruction in clinical routines. Nonetheless, more work is required to investigate standardized methods to validate reconstruction algorithms for future clinical use.

Berkshire Grey shareholders Khosla Ventures, New Enterprise Associates, Canaan Partners and SoftBank Group Corp. will roll 100% of their equity into the combined company, the companies said Wednesday. Berkshire Grey, founded in 2013, develops systems that use artificial intelligence, mobile robots and scanning, gripping and sensing technology to pick orders and speed goods through distribution centers. The business had $35 million in revenue last year and expects to generate $59 million in revenue in 2021 and become profitable in 2024. Top news and in-depth analysis on the world of logistics, from supply chain to transport and technology. Its customers include Walmart Inc., Target Corp. and FedEx Corp.

This article about the best artificial intelligence logistics startups is part of the "Logistics of the Future" series looking at the top logistics startups today. We are officially living in the age of Artificial Intelligence. It's everywhere we look, from AI-powered personal assistants to predictive analytics to making medical diagnoses, Artificial Intelligence is making incredible advances across all industries. In fact, a recent report on the state of Artificial Intelligence for enterprises found that supply chain and operations are some of the top areas where businesses are driving revenue from AI investment. Why is AI making such a big difference in the logistics and supply chain, particularly?

Locus Robotics, a Wilmington, MA-based startup, raised $25 million in a Series B funding led by Silicon Valley Scale Venture Partners, with additional participation from existing investors. Locus plans to use the funds to expand into international markets and build up its growing subscription-based robot fleet. Locus business model uses Robots-as-a-Service (RaaS) which allows customers to use Locus' solutions without a large-scale capital investment. The story of how Locus came to be is almost as interesting as why their mobile robots and RaaS business mode are getting so much attention and acceptance. In March 2012, in an effort to make their distribution centers (DCs) as efficient as possible, Amazon acquired Kiva Systems for $775 million and almost immediately took them in-house.

Locus Robotics Corp., whose robots help workers fulfill e-commerce orders, raised $25 million in a funding round led by Scale Venture Partners that the company plans to use to develop new products and expand into new markets. The Wilmington, Mass.-based company makes software and autonomous mobile robots that sync up with warehouse management systems to help workers handle orders more efficiently by taking the fastest routes through aisles to fill orders more quickly. The robots resemble motorized stools with shelving and touchscreens. They operate in groups and use sensors to navigate through warehouses as workers pick items and move on. Locus, whose customers include Deutsche Post AG's DHL Supply Chain, says its technology can double the number of items that workers pick per hour.