B.1: Isaac Sim - Revision history

Admin ugqr649i at 09:24, 27 December 2024

2024-12-27T09:24:58Z

← Older revision		Revision as of 09:24, 27 December 2024
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	• '''Short Description''':		• '''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.		Nvidia Isaac sim can be used for domain randomisation, which is a technique for training AI algorithms to be robust and generalise well to new environments. In domain randomisation, 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 randomisation. 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		• '''Reference, URL''': https://developer.nvidia.com/isaac-sim

	• '''~~Relevant Domain/Industry~~''': WEEE & Battery		• '''Applicable Business Category''': WEEE & Battery

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

	• '''Type''': Robotics simulator for synthetic data and domain randomisation		• '''Asset Type''': Other, Robotics simulator for synthetic data and domain randomisation

	• '''AI Breadth''': ML, Computer Vision		• '''AI Breadth''': ML, Computer Vision

	• '''Learning Ability''':		• '''Learning Ability''': Reinforcement Learning, Synthetic Data Generation

	• '''Related technologies''': Python API, Universal Scene Description		• '''Related technologies''': Python API, Universal Scene Description

	• '''Applicable Research Area''':		• '''Applicable Research Area''': Collaborative AI

	• '''Applicable Technical Category''':		• '''Applicable Technical Category''': Machine learning

	~~• '''Applicable Business Category''':~~

	~~• '''Asset Type''':~~

	• '''License Information''': Proprietary license		• '''License Information''': Proprietary license
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	• '''Related to circularity and sustainability''': Not directly		• '''Related to circularity and sustainability''': Not directly

	• '''Audience''': Robotics researchers/practitioners, ~~developers~~		• '''Audience''': Robotics researchers/practitioners, Developers

Admin ugqr649i at 09:18, 20 December 2024

2024-12-20T09:18:16Z

← Older revision		Revision as of 09:18, 20 December 2024
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	• '''Related technologies''': Python API, Universal Scene Description		• '''Related technologies''': Python API, Universal Scene Description

			• '''Applicable Research Area''':

			• '''Applicable Technical Category''':

			• '''Applicable Business Category''':

			• '''Asset Type''':

	• '''License Information''': Proprietary license		• '''License Information''': Proprietary license

Admin ugqr649i at 09:27, 2 May 2023

2023-05-02T09:27:15Z

← Older revision		Revision as of 09:27, 2 May 2023
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	• '''Application in relevant Projects/Initiatives''': N.A.		• '''Application in relevant Projects/Initiatives''': N.A.

	• '''Type''': ~~Simulator~~ for ~~Synthetic~~ data and ~~Domain Randomization~~		• '''Type''': Robotics simulator for synthetic data and domain randomisation

	• '''AI Breadth''': ML, Computer Vision		• '''AI Breadth''': ML, Computer Vision
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	• '''Learning Ability''':		• '''Learning Ability''':

	• '''Related technologies''':		• '''Related technologies''': Python API, Universal Scene Description

	• '''License Information''': Proprietary license		• '''License Information''': Proprietary license
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	• '''Related to circularity and sustainability''': Not directly		• '''Related to circularity and sustainability''': Not directly

	• '''Audience''':		• '''Audience''': Robotics researchers/practitioners, developers

Admin ugqr649i at 14:18, 26 April 2023

2023-04-26T14:18:59Z

← Older revision		Revision as of 14:18, 26 April 2023
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	• '''Short Description''':		• '''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
	• '''Reference, URL''':

	• '''Relevant Domain/Industry''': WEEE & Battery		• '''Relevant Domain/Industry''': WEEE & Battery

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

	• '''Type''':		• '''Type''': Simulator for Synthetic data and Domain Randomization

	• '''AI Breadth''':		• '''AI Breadth''': ML, Computer Vision

	• '''Learning Ability''':		• '''Learning Ability''':
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	• '''License Information''': Proprietary license		• '''License Information''': Proprietary license

	• '''Related to circularity and sustainability''':		• '''Related to circularity and sustainability''': Not directly

	• '''Audience''': ~~Manufacturer~~		• '''Audience''':

Admin ugqr649i at 09:56, 26 April 2023

2023-04-26T09:56:36Z

← Older revision		Revision as of 09:56, 26 April 2023
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	• '''Reference, URL''':		• '''Reference, URL''':

	• '''Relevant Domain/Industry''': WEEE/Battery		• '''Relevant Domain/Industry''': WEEE & Battery

	• '''Application in relevant Projects/Initiatives''':		• '''Application in relevant Projects/Initiatives''':

Admin ugqr649i: Created page with " • '''Short Description''': • '''Reference, URL''': • '''Relevant Domain/Industry''': WEEE/Battery • '''Application in relevant Projects/Initiatives''': • ''..."

2023-04-26T09:55:54Z

Created page with " • '''Short Description''': • '''Reference, URL''': • '''Relevant Domain/Industry''': WEEE/Battery • '''Application in relevant Projects/Initiatives''': • ''..."

New page

• '''Short Description''':

• '''Reference, URL''':

• '''Relevant Domain/Industry''': WEEE/Battery

• '''Application in relevant Projects/Initiatives''':

• '''Type''':

• '''AI Breadth''':

• '''Learning Ability''':

• '''Related technologies''':

• '''License Information''': Proprietary license

• '''Related to circularity and sustainability''':

• '''Audience''': Manufacturer

Go back to the '''[[AI portfolio of reference implementations]]'''