Category: Uncategorized

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XploreR – Autonomous Exploration with Virtual Reality Visualization 

The goal of the XploreR project was to apply autonomous exploration using a single robot. In the next step, we combined the autonomous exploration algorithm implemented in a mobile robot with a virtual reality environment for visualizing collected data.

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Training depth estimation model in Nvidia Isaac Sim

Depth estimation plays a big role in robotics, autonomous driving (mapping, localization and obstacle avoidance), augmented reality and many other computer vision technologies across many industries. It is an important phenomenon which helps in understanding and in virtual reconstruction of 3D real world representation by measuring the distance of the scene in the 3D world

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Deadlocks in rclpy and how to prevent them with use of callback groups

Our recent migration of Karelics Brain from ROS 1 to ROS 2 had our whole team learning a lot of new things in a relatively short time period.

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Karelics Oy at ROSDevDay 2021

It is then obvious that when we have heard that a new installment of the RosDevDay conference will also take place in 2021 as an online event, in spite of all the drawbacks imposed by the ongoing COVID pandemic, we have decided that Karelics also had to be there and not by just simple attendance but also by being one of the gold sponsors of the conference.

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The challenges of migrating Karelics Brain to ROS 2. Contributing to the ds4_driver package.

In our latest blogpost we have shown that we have already successfully finished, tested and shown to the world the first version of Karelics Brain.

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Shaping the robotics competence center of Joensuu

At Karelics, one of our main missions is to shed more light on the fact that the 21st century is already shaping up to be the century of robotics and is high time we already made the general public aware to the existence of intelligent robots that can be our day to day helpers and can make our lives better and easier.

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Using Gazebo for Reinforcement Learning

The world is highly complex. To operate in this complex world with self-driving robots, it would be hard to program all the tasks by hand, creating rules for every subtask and action. We humans can complete really complex tasks just by setting for ourselves the final goal for what we are supposed to do.

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Using AR to find the best size of your future robot.

The first question that you face when building a Robot is what should be the best size for it. There are several ways to do it:

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Can Artificial Intelligence play Minecraft?

Last year, I got an incredible opportunity to join a team at University of Eastern Finland and compete in MineRL competition. The competition sponsored by Microsoft aimed to push the boundaries of state-of-the-art reinforcement learning and sample efficient learning.

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From Video Game to Real Robot

Can you train a deep learning agent in video game and then transfer this learned information to a real world robot? How can we handle the differences in visuals and action spaces between the game and reality?

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