​​I'm happy to announce that our team (me, Stepan Konev, Kirill Brodt) was awarded🏅 3rd place within the Waymo Motion Prediction Challenge 2021.

To plan a safe and efficient route, an autonomous vehicle should anticipate future motions of other agents around it. Motion prediction is an extremely challenging task that recently gained significant attention from the research community. We present a simple and yet very strong baseline for multimodal motion prediction based purely on Convolutional Neural Networks.

The task is the following: Given agents' tracks for the past 1 second on a corresponding map, we had to predict the positions of the agents on the road for 8 seconds into the future.

Our model takes a raster image centered around a target agent as input and directly predicts a set of possible trajectories along with their confidences. The raster image is obtained by rasterisation of a scene and the history of all the agents. While being easy-to-implement, the proposed approach achieves competitive performance compared to the state-of-the-art methods on the Waymo Open Dataset Motion Prediction Challenge (2021): Our model ranks 1st using minimum average displacement error and 3rd using mAP score.

We wrote a small paper and release our code!

📜Technical report
⚒Code

Aloha guys!
I'm verty excited to announce that I have joined Facebook Reality Labs (FRL) as a Research Scientist! Before that, I interned twice at Facebook AI Research, and now I will work in the FRL division, which focuses on virtual and augmented reality. Moving from academy to industry, I hope that I will still have enough freedom in choosing research directions 😉.

Here's a very recent article from Googe Brain that uses diffusion models for super-resolution.

The results are shocking! Their model beats the GAN-based SOTA method. The video shows an example of how a 64x64 picture is upscaled to 1024x1024. But no source code yet.

🌀 Project page
📝 Paper

I also wrote about OpenAI paper on diffusion models earlier.

OpenAI disbands its robotics research team. This is exactly the same team that, for example, taught a robotic arm to solve a Rubik's cube using Reinforcement Learning. This decision was made because the company considers more promising research in areas where physical equipment is not required (except for servers, of course), and there is already a lot of data available. And also for economic reasons, since Software as a Services is a business with a much higher margin. Yes, the joke is that the non-profit organization OpenAI is considered more and more about profit. This is understandable because it takes a lot of money to create general artificial intelligence (AGI) that can learn all the tasks that a person can do and even more.

It's no secret that research in the field of robotics is also a very costly activity that requires a lot of investment. Therefore, there are not so many companies involved in this. Among the large and successful, only Boston Dynamics comes to mind, which has already changed several owners. Did you know that in 2013 Google acquired Boston Dynamics, then Google also scaled down its robotics research program, and in 2017 sold Boston Dynamic to the Japanese firm SoftBank. The adventures of Boston Dynamics did not end there, and in December 2020 SoftBank resold 80% of the shares (a controlling stake) to the automaker Hyundai. This looks somehow fishy as if every company understands after a few years that it is still difficult to make a profit from Boston Dynamics and sells it to another patsy.

In any case, it is very interesting to observe which focus areas are chosen by the titans of AI research. But I'm a bit sad that robots are still lagging behind.