![Yahav Avigal on X: "To help robots grasp transparent objects such as glassware and test-tubes, Dex-NeRF combines grasp analysis from Dex-Net with Neural Radiance Fields (NeRF). To appear in #CoRL2021 @UCBerkeley @Berkeley_AI @ Yahav Avigal on X: "To help robots grasp transparent objects such as glassware and test-tubes, Dex-NeRF combines grasp analysis from Dex-Net with Neural Radiance Fields (NeRF). To appear in #CoRL2021 @UCBerkeley @Berkeley_AI @](https://pbs.twimg.com/ext_tw_video_thumb/1453760037273227264/pu/img/lrEuPvF3VkXvhxzO.jpg)
Yahav Avigal on X: "To help robots grasp transparent objects such as glassware and test-tubes, Dex-NeRF combines grasp analysis from Dex-Net with Neural Radiance Fields (NeRF). To appear in #CoRL2021 @UCBerkeley @Berkeley_AI @
GitHub - BerkeleyAutomation/dex-nerf-datasets: Datasets for Dex-NeRF: Using a Neural Radiance field to Grasp Transparent Objects
![Yahav Avigal on X: "As NeRF was not originally intended for planning a grasp on transparent objects, Dex-NeRF extends its capabilities to generate high-quality depth images that allow for robust robot grasp Yahav Avigal on X: "As NeRF was not originally intended for planning a grasp on transparent objects, Dex-NeRF extends its capabilities to generate high-quality depth images that allow for robust robot grasp](https://pbs.twimg.com/media/FCzLqWfVgAQQY2J.jpg:large)