Robotics and Embodied AI Lab (REAL)

The Robotics and Embodied AI Lab (REAL)s is a research lab affiliated with DIRO and Mila, at Université de Montréal. REAL is dedicated to making robots and other embodied agents more capable, easier to use, and safer with machine learning where appropriate.

In particular, REAL is focussed on building representations of the world (such as for simultaneous localization and mapping), modelling of uncertainty, and building better workflows to teach robotic agents new tasks (such as through simulation or demonstration). We are always looking out for talented students to join us as full-time students / visitors. To know more, click on the link below.

Learn more
October 15, 2020

June 05, 2020
Our paper [MapLite: Autonomous intersection navigation without detailed prior maps] was adjudged best Robotics and Automation Letters (RAL) paper for 2019! Check it out here. And, here’s a short video abstract.

January 20, 2020
Check out our new ICRA 2020 paper gradSLAM: Dense SLAM meets automatic differentiation on fully differentiable dense SLAM: Project page, Video.

September 10, 2019
The “Active Domain Randomization” paper got accepted to CoRL 2019. Congrats Bhairav, Manfred, and Florian.

September 01, 2019
Dhaivat, Rey, and Philippe joined the group as Masters’ students. Welcome!

September 01, 2019
Sharath, Mark, Amrut, Rohan, and Dishank joined the group as interns. Welcome!

August 01, 2019
Our paper Deep Active Localization got accepted into Robotics and Automation Letters

November 28, 2018
Dhaivat Bhatt just joined our group as an intern. Welcome!

More news …

Active Domain Randomization active

Making sim-to-real transfer more efficient

Last updated 3 mins ago

Kimplete active

Learning to construct amodal, interactable representations of the world

Last updated 3 mins ago

Deep Active Localization inactive

Learned active localization, implemented on “real” robots.

Last updated 3 mins ago

Efficient Sim-to-real transfer

This project has a lot of people working on it!!

Last updated 3 mins ago

Self-supervised visual odometry estimation inactive

A self-supervised deep network for visual odometry estimation from monocular imagery.

Last updated 3 mins ago

All projects…

© Robotics and Embodied AI Lab Team, 2019

Department of Computer Science and Operations Research | Université de Montréal

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