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ENOT.ai, which stands for “Embedded Neural Network Optimization Technology,” today released its optimization technology for developers of artificial intelligence and edge AI. The Riga, Latvia-based company said its framework can make deep neural networks faster, smaller, and more energy-efficient. “Today ... neural networks are widely used in production and applications,” stated Sergey Aliamkin, founder and CEO of ENOT. “Neural networks…
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…of models is represented by neural networks, thanks to their ability to generalize. Neural networks can adapt to new, previously unseen data and recognize objects that they have never seen before. This capability is widely used in applications that require the recognition and handling of items that differ in shape, size, color, or material. Examples include automated picking of mixed…
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…use mathematical models in a neural network to identify patterns in data and respond to them. This can be applied for pattern recognition in images, videos, sounds, text and any other type of data. In design, integrating deep learning and design processes can help bring better products to market more quickly. “Deep learning and design engineering can learn a lot…
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…built on a hierarchy of neural networks. In the first layer, the system receives input data from sensors and then passes it on to what are called “hidden layers.” In the second tier, the computer performs a succession of computations on the inputs, interpreting sensory data through machine-perception algorithms. Each hidden layer trains on a distinct set of features, based…
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…representation problem is solved with neural networks, and the second is the annotation problem.” “My background was in electrical engineering and deep learning,” he told Robotics 24/7. “Another founder came out of the neurobiology lab. We realized that using a neural network as a classifier is not the right approach. Once you do that, you already limit something predefined by…
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…learning technique called generative adversarial networks (GANs). This approach automatically identifies features or patterns in available input data and then uses the information to generate synthetic data with characteristics similar to physical data from the original dataset. GANs produce realistic data by training a generator network that outputs synthetic data. A discriminator network then tries to classify this output as…
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…company at $2.6 billion. OpenAI neural network improvements in Figure 02 As part of the new humanoid, Figure 02 has a new addition for its artificial intelligence and neural network technology for, “speech-to-speech reasoning,” according to the company. When Figure AI announced its Series B round, including in the investors were OpenAI, Microsoft, NVIDIA and others. Along with the funding,…
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…introduces “Dedicated Tote Detection & Multi-Neural Network” capabilities, which Photoneo said will redefine vision-guided robotics (VGR) intelligence. Following up on its latest MotionCam-3D Color (Blue) launch, Photoneo will also debut an all-new 3D sensor in the blue laser family. The company said the new sensor is engineered to push the boundaries of precision and reliability in industrial automation. Photoneo said…
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…learning. Engineers can now train neural networks in the updated Deep Network Designer app, manage multiple deep learning experiments in a new Experiment Manager app, and choose from more network options to generate deep learning code. R2020a introduces new capabilities specifically for automotive and wireless engineers in addition to hundreds of new and updated features for all users of MATLAB…
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…video) “nerf2nerf: Pairwise Registration of Neural Radiance Fields” (Read paper, watch video) “DexGrasp-1M: Dexterous Multi-Finger Grasp Generation Through Differentiable Simulation” (Read paper, watch video) Large language models among paper topics In addition, NVIDIA will present research on machine learning: “ProgPrompt: Generating Situated Robot Task Plans Using Large Language Models” (Read paper, watch video) “DefGraspNets: Grasp Planning on 3D Fields With…
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…(EEMP). Novel motion-planning techniques like neural networks or machine learning (ML) offer to significantly streamline the creation of motion-control algorithms. These new algorithms could be essential in operating factory robots safely and efficiently. AI for robotics motion planning Creating an effective motion-planning algorithm can be extremely complex, and to date, no one has arrived at one single answer to the…
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…to identify objects using trained neural networks. The video also showed the robot grasping objects at an actual workstation in Tesla's factory in Fremont, Calif. The company said it chose human-like hands with six actuators and 11 degrees of freedom, adaptive grasp, and sensor feedback because most objects are designed for human hands. Tesla developed simulations but then needed to…
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…hybrid approach relying more on neural networks to understand the scene and types of packages and make better decisions on how to pick them and how to orient end effectors. Certain suction cups have better properties for picking loose polybags, and the system knows where to place it and the overall order of picking. For the multi-pick end effector, what…
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…Mujin robot doesn't use a neural net Other companies using AI with picking robots rely heavily on neural networks, but they are very difficult to train, asserted Brandon Coats, director of system innovation at Mujin Inc. “No matter what you do, it will never be 100% accurate,” he said. “So we’re going in and using a model-based definition approach.” That…
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…experimentally measured data to a neural network machine learning system, the scientists developed and “trained” the system to predict samples’ yield strength 20 times more accurately than existing methods. The new analytical technique could reduce the need for time-consuming and costly computer simulations, to ensure that manufactured parts used in structural applications such as airplanes and automobiles, and those made…
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…
