Open AI rang in the new year with a major announcement: two new revolutionary pieces of research: 1)DALL-E which can generate images from text, and 2)CLIP which provides a one-shot image classification approach without the requirement of training a model. This article focuses on CLIP, specifically, how the Vector robot can classify objects that it sees as long as an input list of possible text sequences that describe the expected objects is provided.
For most of my career, I have been an engineer solely focused on the performance aspects of computer operating systems. Three years ago, I started driving an analytics and AI intensive feature for an emerging product at my current employer. Learning the subtleties of Artificial Intelligence (AI) was an uphill struggle for me. Luckily, at the same time, I was also a beta tester for a robotics company called Anki, who were releasing their first full fledged toy robot, the Cozmo. Along with Cozmo, Anki also released a full fledged SDK to provide the ability to program Cozmo using…
In my last article, we examined how OpenAI CLIP can classify an image amongst multiple options of provided text (prompts) using a pre-trained model; thus providing the ability of zero shot image classification. We have also examined how Roboflow.ai helps you automate your data ingestion pipeline by providing you with a large variety of data preprocessing and augmentation techniques and the ability to export the dataset in multiple formats.
One of the most popular videos in my YouTube channel is an illustration of how one can draw on Cozmo’s face by simply moving the cube around. The program is simple to write using the Cozmo Codelab. But there are very important concepts to learn here. One of the most essential jobs of commercial robots is to grasp and move objects. Behind this technology is the art of determining the pose of an object, known as 6D Object Pose Estimation (6DOPE). Before, going into a deep dive on 6D OPE, I would first like you to review the following video.
In my previous post, I discussed how Roboflow.com features the Anki Vector public dataset, which can be used to train the Vector too recognize another Vector using the Anki Vector SDK. This post captures how you can add your own images to the dataset, and build your own dataset. More images add more diversity to the dataset, and lead to better trained models. Here is what you will have to do.
You will need the following:
Recently, I published an article in “Towards AI” on how to train a YOLO v5 model that can be used by Vector to detect another Vector. Brad Dwyer, founder of Roboflow, read the article and reached out to me to offer a free upgraded Roboflow account which provides for all the functionality that Roboflow including the ability to release publically available datasets. Thanks Brad!
So that motivated me to work further on this. As I have documented before, the main barrier to using supervised Machine Learning (ML) aka Deep Learning (DL)is the requirement of large volumes of high quality labelled…
This tutorial teaches you the basics of object detection via the YOLO (You Only Look Once) algorithm, which is a state-of-the-art, real-time object detection system. The original YOLO paper is available here. The official YOLO repository is here. While this tutorial does not require a thorough understanding of how YOLO works, here is a great article if you are really interested in a deep dive. This tutorial uses the most recent generation of YOLO (called YOLOv5). An interesting introduction to YOLOv5 is available here.
This tutorial also makes use of the Anki Vector robot (assets currently owned by Digital Dream…
This week, I tried Vector Connect, an app developed by the Robots for Good initiative, a project by the Social Robotics Lab run by Prof. Brian Scassellati at Yale University. This is a pretty cool app (probably the only Vector app that exists outside of the official one that was developed by Anki Inc.). On the left is a screenshot from the app. In summary, the app allows three different things:
Avid biker. VMware engineer. Robotics. Thoughts in this forum reflect my own opinions. Write about Robotics, Vector, Cozmo, and VMware.