Kenneth Blomqvist

I used to be a software engineer at Wolt, Webflow and Flowdock. I founded the Junction hackathon at the Aalto Entrepreneurship Society.

Now, I build software for robots at the Autonomous Systems Lab. Currently, I'm working on closing the perception-control loop to make robots that can robustly manipulate objects and respond to what is happening around them.


Collecting RGB-D Datasets on LiDAR Enabled iOS devices

10 Mar 2021

We Need Organic Software

29 Nov 2020

Some Projects

Stray Scanner App

Stray Scanner is an app for collecting RGB-D datasets with an iPhone or iPad with a LiDAR sensor.

Mobile Manipulation Demo

We created a simple demo to investigate the limitations of state-of-the-art methods in mobile manipulation. The robot is tasked to go through an office to find an object, pick it up and return with the object. It uses SLAM, motion planning, grasp planning and some perception algorithms to get the job done.

We wrote about the work here. A video of the demo can be found here.

Particle Simulation

We implemented a particle based material simulation to simulated deformable and grainy materials as well as fluids. I later extended it to make use of CUDA.

Check out the demo here. Code is available here.

Autonomous Race Car

We built an autonomous race car using an old 1/10th scale radio-controlled touring car. It uses an Nvidia Jetson TX 1 which communicates with some rc electronics through an Arduino board. It has an RGB camera at the front and an IMU sensor. Velocity is measured through a sensor inside the brushless motor.

The car be both be driven using the regular RC remote or autonomously reading commands from the Jetson. The commands from the remote can be recorded and used for learning. The picture is from an early version which used a Raspberry Pi instead of the Jetson.

We used the platform to do some research into driving the car using reinforcement learning. Most of the code is available here.

Deep Convolutional Gaussian Processes

In this project, we basically tried to build a modern convolutional neural network using Gaussian processes. We ran the model on some image classification benchmarks. At the time, we were able to get better results than any other GP based method, but results are still behind the advanced neural network based techniques. Scaling these large GP models still remains a challenge.

The results can be found in our paper and code is available here.

Get in touch

You can find me on GitHub, Twitter, LinkedIn and Goodreads. Feel free to get in touch via email.