Got nerd-sniped into robotics research 2 months ago with no prior experience Ended up reimplementing computer vision on my Roomba with SLAM and now it's mapping the lab like a mini-explorer Here are my 3 most surprising takeaways:

#1 - Many tasks in robotics are better done without ML Coming from a software/ML background, I asked myself why traditional robotics hasn't been replaced with ML. I learned that ML often comes at the cost of speed & predictability over many traditional statistical approaches.

#2 - ROS[2] makes robot SW a lot more fun To get this to work, I had to convert an existing SLAM algo developed by Kevin Hu (VIP Lab) here at UWaterloo to ROS2. It made me realize how much of the grunt work ROS2 alleviates from someone just trying to ship a robotics project.

#3 - You learn much more by debugging and trying to figure shit out than following a course The main difficulty in this project was surprisingly a network issue through docker and navigating a complex build system. None of these are taught in ROS courses. ...

I did 3 out of a 22-hour ROS course ( and just jumped into porting the SLAM codebase I was given to ROS2. It takes much less time to get started than I thought.

I'd highly recommend playing around with robotics - think of an easy project in actuation/perception/control and take the first step. I'm very bullish on humanoid robotics, and psyched to play around with more robotics stuff when I'm back in the loo post-coop 🥘

banger...that's so cool build brother 🔥

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You should check out what @mehul is up to with @maticrobots

So let’s say I want to make a robot to pick up/shovel a furball off the floor in a simple rectangular room. How impossible is that

1. Use SLAM (what I just implemented) to map the room and locate the robot 2. Add object detection to identify the furball 3. Use path planning to navigate to the furball 4. Implement a simple grasping or shoveling mechanism (matic robots implements something like this afaik)