187: Self-Driving Arm

Embedded.fm · 15 Feb 2017 · 1h 12m

AI in practice: 3 real uses of AI described

What this episode covers

Patrick Pilarski, a Canada Research Chair at the University of Alberta, discusses his work on smart prosthetic limbs that use machine intelligence to bridge the gap between limited biological signals and complex robotic actuators. He explains how reinforcement learning allows prostheses to learn from trial and error in real-time, using techniques like 'prosthetic falconry' where a robotic limb learns by observing a user's healthy biological arm. Pilarski highlights the shift from simple myoelectric controls to systems that predict user intent and context, such as using cameras to identify objects like soup or doorknobs to pre-shape grips. He also outlines accessible tools for engineers, including Weka, OpenIA Gym, and Robotis Dynamixel servos, to get started with reinforcement learning robotics.

AI in practice

Where this conversation describes AI being used to build or deliver a product. Part of the AI in practice tracker.

Engineering & simulationCo-AP

A company called Co-AP uses pattern recognition, a form of machine learning, to map muscle contraction patterns to prosthetic limb movements, allowing users to control their limbs more intuitively and reliably than with traditional methods.

“There's a company … spun off a company called Co-AP … doing, essentially, uh, pattern recognition. So they're using a classification system that allows people to um, essentially deploy pattern recognition. That's what it's called, as a form of machine learning, in prosthetic limb control.”

Speaker 3 · Hear it at 52:53

Engineering & simulationWeka

The guest recommends using the Weka data mining toolkit to help engineers understand their sensor data and experiment with supervised learning methods without needing to write complex code from scratch.

“I usually point them in the direction of something like uh, Weka. It's the data mining toolkit from New Zealand. It's a free open source Java toolkit. It has almost every major sort of supervised learning, machine learning method that you might want to play with.”

Speaker 3 · Hear it at 1:03:46

Engineering & simulationOpenAI Gym

The guest suggests OpenAI Gym as a platform for students and engineers to try out reinforcement learning algorithms and get familiar with machine learning methods in a simulated environment.

“Open AI gym is another really great, great resource. Open AI gym is a new platform where you can try out things like reinforcement learning as well. My students have been using it, and it's really, really, um, pretty functional in a very, Quick amount of, in a quick ramp up cycle.”

Speaker 3 · Hear it at 1:03:46

Mentioned

Companies
University of Alberta, Alberta Machine Intelligence Institute, Thalmic Labs, Co-AP
Products
Myo, Cybathlon
Chips
Robotis Dynamixel
Tools
Weka, OpenIA Gym

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