B.1: Isaac Sim

• Short Description: Nvidia Isaac sim can be used for domain randomization, which is a technique for training AI algorithms to be robust and generalize well to new environments. In domain randomization, the training data is generated by simulating various variations of the environment, such as changes in lighting conditions, object appearances, and other factors. This can help the AI model learn to be robust to these variations and perform well in a range of different environments. Isaac sim provides tools for easily creating and manipulating these variations in the simulated environment, making it a useful tool for domain randomization. Additionally, it provides tools for generating realistic sensor data from the simulated environment, which can be used to train and evaluate AI algorithms.

• Reference, URL: https://developer.nvidia.com/isaac-sim

• Relevant Domain/Industry: WEEE & Battery

• Application in relevant Projects/Initiatives: N.A.

• Type: Robotics simulator for synthetic data and domain randomisation

• AI Breadth: ML, Computer Vision

• Learning Ability:

• Related technologies: Python API, Universal Scene Description

• Applicable Research Area:

• Applicable Technical Category:

• Applicable Business Category:

• Asset Type:

• License Information: Proprietary license

• Related to circularity and sustainability: Not directly

• Audience: Robotics researchers/practitioners, developers

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