Found in News and Content
…They are powered by robust neural networks that were trained on thousands of items to be able to recognize boxes and goods of various shapes, sizes, colors, materials, and textures, said Photoneo. “The [Depalletization] algorithms can also easily recognize boxes that are very tightly packed,” said Pufflerova. “This is often a big challenge for automation systems, as it is difficult…
Found in News and Content
…taking noisy data. Our deep neural networks use insect-like vision to create structured 3D geometry. We're looking for AI intelligent animals versus Web-based robotics.” Electric Sheep grows through acquisition The acquisitions of Phenix Landscape and Complete Landscape will help Electric Sheep to grow eightfold, predicted the company. It plans to offer full services including data collection rather than just robotics…
Found in News and Content
…and RL models to create neural networks that automate yard tasks with increasing intelligence, precision and speed. Outrider’s private AI cloud deployment utilizes NVIDIA’s DGX H200 GPUs installed at a Denver-based data center owned and operated by Equinix. “When dealing with exponentially increasing amounts of data to train DL and RL models, processing speed and training velocity per dollar spent…
Found in Downloads
…valuable data for humans and neural networks to sift through, organizations can accurately monitor and track their inventory and workflows. When combined with generative AI analytics, robots can serve as mobile data platforms, informing decision-making and improving operational efficiency for commercial and industrial end users. In this Special Focus Issue Dexory delivers real-time visibility for warehouse operators Nokia AIMS automates…
Found in News and Content
…Neurosymbolic AI & VDA5050: Hybrid neural networks for perception combined with symbolic reasoning for planning, with VDA5050‑compliant fleet orchestration for vendor‑agnostic interoperability. Edge‑to‑cloud orchestration: NVIDIA Jetson at the edge for sub‑30 ms latency decisions, with cloud GPUs for digital twins, simulation and V2X integration. Asynchronous delivery ecosystem: Unattended pickup and drop‑off via smart receptacles and multimodal logistics combining ground robots…
Found in News and Content
…unsupervised learning technique that enables neural networks to learn directly from massive amounts of data, bypassing the need for costly human annotation. Paired with semantic simulation, Helm.ai said that the system can train on practically infinite geometric scenarios without the computational overhead of rendering photorealistic pixels. By training the system on the “semantic geometry” of the world rather than raw…
Found in News and Content
…AI can outperform traditional convolutional neural network (CNN)-based models, NVIDIA noted. “Legacy CNNs are rigid and rule-based and require lots of labeled data, slowing the development cycle,” said Talla. “Generative AI is generalizable and with natural-language prompts, anyone can get the right output.” Generative AI could add $10.5 billion in revenue for manufacturing operations worldwide by 2033, predicted ABI Research.…
Found in News and Content
…paper, “An Intelligent Hybrid Artificial Neural Network-Based Approach for Control of Aerial Robots,” in the Journal of Intelligent & Robotic Systems. It outlines a proposed hybrid control approach that would use AI to enhance the controller so that these autonomous drones could better respond to emergency situations. Kayacan says this hybrid model will benefit from the use of simulation and…
Found in News and Content
…that works as a serverless neural network where communications take place without the need of a central server. This provides a new level of redundancy, as there is no danger of attack at one node that may compromise the rest of the network. The Factory Management module offers multiple packages to integrate Smart3D systems with the company’s ERP, enabling management…
Found in News and Content
…artificial intelligence and inventory and network optimization. In 2017, the Massachusetts Institute of Technology tested drone technology using RFID tagged items. Drones could read RFID tags tens of meters away with an average error of only 19 centimeters - quite accurate given the distance. Researchers said the goal was to prevent inventory mismatches and locate individual items. “If you're carrying…
Found in News and Content
…recyclables and run on the network in real time,” said Jason Calaiaro, co-founder and head of engineering at AMP Robotics. “The end-to-end workflow from NVIDIA can train the neural network. We're using TensorRT and increased performance tenfold. Think of different configurations of cans—some can be crushed or in different positions. We can now recycle 90% of that garbage.” Erik Nieves,…
Found in News and Content
…simulation models to train a neural network used for active monitoring and prediction of anomalies of a hydraulic press. Using simulation models to generate data representing faulty machines allowed them to overcome the difficulty posed by a lack of real failure data from their machines. This makes it clear that there is a great opportunity for companies to bring together…
Found in News and Content
…a video camera. An artificial neural network with some post-processing algorithms builds up a dynamic picture of the general surroundings, providing a trajectory for vehicle motion.” “We also focus on radar because of several challenges,” he said. “There's quite a debate over lidar versus radar, but agricultural machinery operates in harsh conditions, with dust, hay, and earth. They could significantly…
Found in News and Content
…the Cortex AI deep learning neural network, can transform any physical space into a dimensionally accurate and photorealistic digital twin. More than 330,000 subscribers in over 150 countries have captured data on over 5 million spaces to better access, manage, and understand spaces, from a single property to a global portfolio of buildings. Matterport said it hosts the largest spatial…
Found in News and Content
…claimed that AMP Neuron's artificial neural network “has created the largest known real-world dataset of recyclable materials for machine learning.” With the power of this data, AMP said it has achieved two milestones: The company can now classify more than 100 different categories and characteristics of recyclables across single-stream recycling, e-scrap, and construction and demolition debris. AMP has extended its…
Ultrasonic sensing enhances robotics perception
Cybernetix Ventures’ event kicks off Robotics Tech Week 2026 slate of events
Preview the manufacturing and warehouse components that will be on the…
Preview the manufacturing and warehouse robots and software that will be on…
