Posts published in “Computing”
The art and science of mechanized thought.
"Robots in Montreal," eh? Sounds like the title of a Steven Moffat Doctor Who episode. But it's really ICRA 2019 - the IEEE Conference on Robotics and Automation, and, yes, there are quite a few robots!
My team presented our work on evolutionary learning of rewards for deep reinforcement learning, AutoRL, on Monday. In an hour or so, I'll be giving a keynote on "Systematizing Robot Navigation with AutoRL":
Keynote: Dr. Anthony Francis
Systematizing Robot Navigation with AutoRL: Evolving Better Policies with Better Evaluation
Abstract: Rigorous scientific evaluation of robot control methods helps the field progress towards better solutions, but deploying methods on robots requires its own kind of rigor. A systematic approach to deployment can do more than just make robots safer, more reliable, and more debuggable; with appropriate machine learning support, it can also improve robot control algorithms themselves. In this talk, we describe our evolutionary reward learning framework AutoRL and our evaluation framework for navigation tasks, and show how improving evaluation of navigation systems can measurably improve the performance of both our evolutionary learner and the navigation policies that it produces. We hope that this starts a conversation about how robotic deployment and scientific advancement can become better mutually reinforcing partners.
Bio: Dr. Anthony G. Francis, Jr. is a Senior Software Engineer at Google Brain Robotics specializing in reinforcement learning for robot navigation. Previously, he worked on emotional long-term memory for robot pets at Georgia Tech's PEPE robot pet project, on models of human memory for information retrieval at Enkia Corporation, and on large-scale metadata search and 3D object visualization at Google. He earned his B.S. (1991), M.S. (1996) and Ph.D. (2000) in Computer Science from Georgia Tech, along with a Certificate in Cognitive Science (1999). He and his colleagues won the ICRA 2018 Best Paper Award for Service Robotics for their paper "PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning". He's the author of over a dozen peer-reviewed publications and is an inventor on over a half-dozen patents. He's published over a dozen short stories and four novels, including the EPIC eBook Award-winning Frost Moon; his popular writing on robotics includes articles in the books Star Trek Psychology and Westworld Psychology. as well as a Google AI blog article titled Maybe your computer just needs a hug. He lives in San Jose with his wife and cats, but his heart will always belong in Atlanta. You can find out more about his writing at his website.
Looks like I'm on in 15 minutes! Wish me luck.
Re: Whassap? Gordon: Sounds like a plan. (That was an actual GMail suggested response. Grumble-grumble AI takeover.) Anthony: I<tab-complete> welcome our new robot overlords.I am constantly amazed by the new autocomplete. While, anecdotally, autocorrect of spell checking is getting worse and worse (I blame the nearly-universal phenomenon of U-shaped development, where a system trying to learn new generalizations gets worse before it gets better), I have written near-complete emails to friends and colleagues with Gmail's suggested responses, and when writing texts to my wife, it knows our shorthand! One way of doing this back in the day were Markov chain text models, where we learn predictions of what patterns are likely to follow each other; so if I write "love you too boo boo" to my wife enough times, it can predict "boo boo" will follow "love you too" and provide it as a completion. More modern systems use recurrent neural networks to learn richer sets of features with stateful information carried down the chain, enabling modern systems to capture subtler relationships and get better results, as described in the great article "The Unreasonable Effectiveness of Recurrent Neural Networks". -the<tab-complete> Centaur
- Listening To: Tomb Raider soundtrack (the original).
- Reading: Theoretical Neuroscience (book).
- Writing: "Death is a Game for the Young", a novella in the Jeremiah Willstone multiverse.
- Editing: SPECTRAL IRON, Dakota Frost #4.
- Reviewing: SHATTERED SKY, Lunar Cycle #2 by David Colby.
- Researching: Neural Approaches to Universal Subgoaling.
- Programming: A toy DQN (Deep Q Network) to stretch my knowledge.
- Drawing: Steampunk girls with goggles.
- Planning: Camp Nanowrimo for April, ROOT USER, Cinnamon Frost #3.
- Taking on: Giving up alcohol for Lent.
- Dragging on: Doing my taxes.
- Spring Cleaning: The side office.
- Trying to Ignore: The huge pile of blogposts left over from GDC and CA.
- Caring For: My cat Lenora, suffering from cancer.
- Waiting For: My wife Sandi, returning from a business trip.