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Published: July 27, 2017, 3:16 a.m.

Elecia gave a talk about machine learning and robotics at the Hackaday July Meetup\xa0at SupplyFrame DesignLab (video!) and LA CrashSpace. She gives it again in the podcast while Chris narrates the demos.\xa0

Embedded Patreon

Embedded show #187: Self Driving Arm\xa0is the interview with Professor Patrick Pilarski about machine learning and robotics applied to prosthetic limbs.

I have also written more about my machine learning + robot arm on this blog. My code is in github (TyPEpyt).

My machine learning board is Nvidia\u2019s Jetson TX2. The Two Days to a Demo\xa0is a good starting point. However, if you are new to machine learning, a better and more thorough introduction is the Andrew Ng\u2019s Machine Learning course on Coursera. To try out machine learning, look at Weka Data Mining Software in Java\xa0for getting to know your data and OpenIA Gym\xa0for understanding reinforcement learning algorithms

I use the MeArm for my robot arm. For July 2017, the MeArm\xa0kit is on sale at the Hackaday store\xa0with the 30% off coupon given at the meetup (or in Embedded #207).

Inverse kinematics is a common robotics problem, it took both Wiki\xa0and this blog post\xa0to give me some understanding.

I wasn't sure about the Law of Cosines before starting to play with this so I made a drawing to imprint it into my brain.

Robot Operating System\xa0(ROS) is the publisher-subscriber\xa0architecture and simulation system. (I wrote about ROS on this blog.) To learn about ROS, I read O\u2019Reilly\u2019s Programming Robots with ROS\xa0and spent a fair about of time looking at the robots on the ROS wiki page.

I am using OpenCV\xa0in Python to track the laser.\xa0Their official tutorials\xa0are an excellent starting point. I recommend Adafruit\u2019s PCA9685 I2C PWM/Servo controller\xa0for interfacing the Jetson (or RPi) to the MeArm.

Finally, my talk notes and the Hackaday Poster!

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