Physical AI-powered robots need to autonomously sense, plan, and perform complex tasks in the physical world. These include transporting and manipulating objects safely and efficiently in dynamic and unpredictable environments. Robot simulation enables developers to train, simulate, and validate these advanced systems through virtual robot learning and testing. It all happens in physics…
]]>Synthetic data can play a key role when training perception AI models that are deployed on autonomous mobile robots (AMRs). This process is becoming increasingly important in manufacturing. For an example of using synthetic data to generate a pretrained model that can detect pallets in a warehouse, see Developing a Pallet Detection Model Using OpenUSD and Synthetic Data.
]]>From building cars to helping surgeons and delivering pizzas, robots not only automate but also speed up human tasks manyfold. With the advent of AI, you can build even smarter robots that can better perceive their surroundings and make decisions with minimal human intervention. Take, for instance, an autonomous robot used in warehouses to move payloads from one place to another.
]]>Collecting a variety of data is important for AI model generalization. A good dataset consists of objects with different perspectives, backgrounds, colors, and sometimes obstructed views. The model should learn how to handle outliers or unseen scenarios. This makes the data collection and labeling process hard. In this post, we showcase sim2real capabilities of NVIDIA Isaac Sim for the…
]]>Deep learning is being adopted in robotics to accurately navigate indoor environments, detect and follow objects of interest, and maneuver without collisions. However, the increasing complexity of deep learning makes it challenging to accommodate these workloads on embedded systems. While you can make trade-offs between accuracy and deep learning model size, compromising accuracy to meet real-time…
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