Found in Robotics News & Content, with a score of 19.01
August 03, 2023
…of Slamcore, in a release. “We provide a reference neural network for identifying objects but have designed this feature with flexibility in mind. Customers can use our API to integrate their own networks and start to truly harness the power of semantic classification in 3D space.” Perceive now widely available The Perceive functions were previously available only through direct customer engagements with Slamcore. As a standard art of the SDK, they “allow many more developers to quickly add the power of semantic segmentation to their products,” said the company. Slamcore explained that Perceive builds on Position and Mapping functions to…
Found in Robotics News & Content, with a score of 18.65
August 16, 2019
…supported, and Jetson Nano is capable of running multiple neural networks in parallel to process data and drive action. Each SparkFun JetBot AI Kit includes the following: NVIDIA Jetson Nano Developer Kit; Robot Platform Chassis and all Prototyping Electronics; 64GB MicroSD card—pre-flashed with the JetBot Image; 145 Field of View, wide-angle camera module; Wi-Fi adapter; serial controlled motor driver; and micro OLED Breakout. SparkFun is accommodating individuals who already own a Jetson Nano Developer Kit by also releasing a version of the JetBot Kit without one. If people are looking to get started with AI but don’t know how, an…
Found in Robotics News & Content, with a score of 18.28
March 01, 2019
…an iterative, three-layered process, 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 on the previous layer’s output. During this process, the algorithms label and cluster raw input according to similarities among the example inputs. They then classify data when they have a labeled dataset to train with. The…
Found in Robotics News & Content, with a score of 18.13
October 01, 2022
…Tesla's Autopilot self-driving software 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 compare them with reality for state estimation, said Anand Swaminathan, staff controls engineer at Tesla. “Reality is way more complex, with vibrations, modes, compliance, and sensor noise,” he said. “We compared what we want and reality…
Found in Robotics News & Content, with a score of 17.13
December 20, 2022
…and even writing code. “Language models are a giant neural network that has read the entire Internet,” Schluntz told Robotics 24/7. “They were given the simple task of predicting the next word, and then that algorithm became 1,000 times larger than any other neural network. They can now predict answers to questions.” “However, what AI is good at is different from what we expected it to be good at,” he noted. “As OpenAI opened up the API, I've played with it. It can pass a Turing Test, but it still makes mistakes. What's really exciting is using language models as…
Found in Robotics News & Content, with a score of 17.10
January 24, 2022
…for several countries at once,” she added. “Our programmers, neural network specialists, and engineers have done a tremendous job.” Cognitive Pilot an autonomous driving joint venture of Sberbank and Cognitive Technologies Group. The Moscow-based company has developed machine vision and artificial intelligence for autonomous equipment for agriculture, rail transport, and urban transportation. Source: Cognitive Pilot Robots on the rails for safety Ensuring safe operations is a key challenge for the global railway transport industry, noted Cognitive Pilot. Accidents involving people or vehicles on railway tracks occur almost every 2 hours, according to the U.S. National Transportation Safety Board. Analysts and…
Found in Robotics News & Content, with a score of 17.00
February 25, 2023
…itself. She first used reinforcement learning to train a neural network, which in turn taught the robot to use extrinsic dexterity to grasp objects in its surrounding environment. In this case, the robot manipulates the object in its gripper with the help of the wall or other vertical surface. Previous research on extrinsic dexterity and robotics often designed systems for grasping objects based on assumptions about how a robot would contact an item. Sometimes, these systems also required more complicated hands or robots. Zhou used AI to develop a system with fewer hardware restrictions that could work for many different…
Found in Robotics News & Content, with a score of 16.82
December 31, 2021
…learning capabilities, he said. 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 approach relies on a combination of sensing the environment and then using it for measurements, path planning, and execution. This enables a picking robot to have a target and a plan to reach the target before…
Found in Robotics News & Content, with a score of 16.80
February 21, 2022
…is very exciting. By joining forces with Traptic, Bowery's network of smart indoor farms will achieve another level of technological sophistication and maturity.” Traptic robots use 3D cameras and neural networks to determine whether fruits are ripe or not, according to Bowery. Its robots are also equipped with grippers and AI to help them pick and harvest fruits and vegetables delicately. “Traptic's technology works 24 hours per day, reduces food waste up to 20%, and increases yields of high-quality, blemish- free produce,” Bowery said in the release. While headquartered in Manhattan, Bowery has an indoor farm and research facility in…
Found in Robotics News & Content, with a score of 16.51
March 15, 2022
…need to avoid other robots and people.” Using deep neural network strategies—types of machine learning techniques—RIT researchers trained the system components to make specific, sequenced decisions based on common tasks, but also infrequent or unusual actions that might occur in the warehouse environment. The team is also studying Wi-Fi and cellular communications networks within warehouses as viable solutions. New standards for cellular technologies permit increased individual cellular communication between individual devices, Kuhl explained. “In terms of people and vehicles interacting, could we take advantage of the sensors of multiple vehicles moving around the warehouse?” he said. “If a vehicle is…
Found in Robotics News & Content, with a score of 16.13
July 31, 2021
…Automation in East Amherst, N.Y., and the Future Mobility Network in Delft, the Netherlands, this week said they have launched Europe's first fully autonomous robotic taxicab service. The partners said the launch opens the door for cities across the EU to adopt this environmentally friendly alternative form of transportation. The robotaxi is an autonomous electric ferry that is solar-powered and can be hailed with a ridesharing app. Buffalo Automation noted that it has been commercially deployed, carrying passengers in dense European traffic. The ferry service dubbed “Vaar met Ferry,” is subsidized by the Dutch provincial government and will be cost-free…
Found in Robotics News & Content, with a score of 16.09
November 16, 2020
…obstacle via a deep 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 either genuine physical data or synthetic data. The two networks essentially train each other until the discriminator network is fooled about half the time, indicating that the generator network is producing plausible data. The GAN approach…
