Found in Robotics News & Content, with a score of 8.13
…training of machine learning (ML) models by exposing the neural network to a wide variety of domain parameters in simulation. Isaac Sim can ensure that the synthetic dataset contains sufficient diversity to drive robust model performance, said NVIDIA. This will help the model to generalize when it encounters real-world scenarios. “If we can throw in a lot of data—say, by varying the color or location of a garbage can—we can test what's important for the robot to understand or to ignore,” explained Andrews. NVIDIA said Isaac Sim open beta enables users to define a region for randomization. Developers can now…
Found in Robotics News & Content, with a score of 8.04
…models created with generative 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. “Generative AI will significantly accelerate deployments of AI at the edge with better generalization, ease of use, and higher accuracy than previously possible,” Talla said. “This largest-ever software expansion of our Metropolis and Isaac frameworks on…
Found in Robotics News & Content, with a score of 8.04
…is to enhance ML model training by exposing the neural network to a wide variety of domain parameters in simulation. This helps the model to generalize well when it encounters real world scenarios. In effect, this technique helps teach models what to ignore. Randomizable parameters in NVIDIA Isaac Sim: Color Movement Scale Light Texture Material Mesh Visibility Rotation Train the ML models using NVIDIA TAO Toolkit Below are pretrained models and proprietary data (real or synthetic) as inputs and a customized model as the output. Functional block diagram of TAO Toolkit. Source: NVIDIA The image below shows that the simulator…
Found in Robotics News & Content, with a score of 8.03
…where learning helps because we can run a lightweight neural network and train it to process noisy sensor data observed by the moving robot.” “This is in stark contrast with most robots today,” he added. “Typically, a robot arm is mounted on a fixed base and sits on a workbench with a giant computer plugged right into it. Neither the computer nor the sensors are in the robotic arm! So the whole thing is weighty, hard to move around.” CSAIL's Improbable AI Lab has developed DribbleBot. Source: MIT More work to do There's still a long way to go in…
Found in Robotics News & Content, with a score of 7.87
…does PIGINet avoid those predefined rules? PIGINet is a neural network that takes in “Plans, Images, Goal, and Initial facts,” then predicts the probability that a task plan can be refined to find feasible motion plans. In simple terms, it employs a transformer encoder, a versatile and state-of-the-art model designed to operate on data sequences. The input sequence, in this case, is information about which task plan it is considering, images of the environment, and symbolic encodings of the initial state and the desired goal. The encoder combines the task plans, image, and text to generate a prediction regarding the…
Found in Robotics News & Content, with a score of 7.87
…said Cognitive Pilot. By using a deep learning convolutional neural network fine-tuned for agronomic purposes, it can understand the types and positions of objects facing the machinery, build movement trajectories, and send commands to perform maneuvers. From June to October 2020, over 350 New Holland, John Deere and Claas autonomous combines equipped with the Cognitive Agro Pilot system harvested 590,000 metric tons of grain crops from over 130,000 hectares (321,000 acres). Some 130,000 metric tons of row and roll crops were harvested from over 30,000 hectares (74,100 acres) in various regions across Russia, added Cognitive Pilot. The system can shorten…
Found in Robotics News & Content, with a score of 7.20
…wider range of data, we might find that a neural network could learn to better generalize across the full scope of the problem,” said Worker and Gupta. The appearance gap can be further closed with high-fidelity 3D assets and ray tracing or path tracing-based rendering, using physically based materials such as those defined with the Material Definition Language (MDL). Validated sensor models and DR of their parameters can also help here. How to set a synthetic scene The building information model (BIM) of an indoor scene was imported into Isaac Sim from Trimble SketchUp through the NVIDIA Omniverse SketchUp Connector.…
Found in Robotics News & Content, with a score of 6.49
…Emelyanov. “The first is 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 decrease visibility.” “Another challenge is the changing landscape,” Emelyanov added. “To understand highlands and evaluate the geometry of the path quite precisely, it's necessary to provide distances with a high grade of reliability at twilight or…
Found in Robotics News & Content, with a score of 6.41
…this, they’re using AWS technologies such as AI for neural networks and computer vision, data streaming, storage and analytics from the edge to the cloud. But how do we make this technology “just work” for the people who are operating or working alongside the robots? One of the key behind-the-scenes components is what the Amazon Robotics team calls “comprehensive device management,” or CDM. It’s the underpinning of how Amazon Robotics and its many development teams are able to rapidly grow, monitor and manage their fleets at scale. And rather than the sometimes flashy and most talked about technologies, it’s back-end…
Found in Robotics News & Content, with a score of 5.23
…more complex robotic tasks RIOS’s AI software uses artificial neural network technology that mimics how brains function in determining how to grasp and handle items, Smith explained. “Such techniques are required given the complexity and challenge of piece-picking use cases, which can include high SKU variation, changing environmental and visual conditions, difficult visual properties involved with certain SKUs, fast cycle times, and precision and accuracy requirements.” In addition, RIOS offers hybrid-cloud software for real-time monitoring of cell performance, such as count statistics, while offering application programming interfaces (APIs) for integration to WMS. RIOS partners with FANUC for articulating robotic arms,…