Principle-centric AI, TWIMLAI Podcast, June, 2022
Introducing a new framework for embodied intelligence I call Principle-centric AI: leveraging (and learning) inductive priors to guide learning towards better outcomes (safety, efficiency, robustness).
Principle-centric AI for Embodied Intelligence, ICRA, May, 2022
Why principles might help taking ML to the next level for embodied platforms and how we have successfully used them so far. Invited talk at the ICRA 2022 work on “Fresh Perspectives for Autonomous Driving”
Advancing ML Research in Autonomous Vehicles, Gradient Dissent Podcast, Dec, 2021
A fun interview where I share my approach to building teams and taking state-of-the-art research from conception to production.
The 3 R’s and 3 P’s of Autonomous Driving, RSS, July 2020
Overview of recent ML Research at TRI across the full robotics stack – (Robust) Perception, (Random) Prediction, and (Risk-aware) Planning – at the RSS 2020 Interaction and Decision-Making in Autonomous-Driving workshop (IDA).
AWS re:Invent 2018: Advancing Autonomous Vehicle Development Using Distributed Deep Learning at TRI, Nov. 2018
A talk Chetan Kapoor, Mike Garrison, and I gave in Las Vegas at AWS re:Invent 2018 about our large scale ML infra, one of the first to do distributed deep learning with PyTorch on AWS p3 GPU instances.