Research Projects

f-Cal - Calibrated aleatoric uncertainty estimation from neural networks for robot perception

f-Cal is calibration method proposed to calibrate probabilistic regression networks. Typical bayesian neural networks are shown to be overconfident in their predictions. To use the predictions for downstream tasks, reliable and calibrated uncertainity estimates are critical. f-Cal is a straightforward loss function, which can be employed to train any probabilistic neural regressor, and obtain calibrated uncertainty estimates.

Collaborators:

Inverse Variance Reinforcement Learning

Improving sample efficiency in deep reinforcement learning by mitigating the impacts of heteroscedastic noise in the bootstraped target using uncertainty estimation.


Lifelong Topological Visual Navigation

A learning-based topological visual navigation method with graph update strategies that improves lifelong navigation performance over time.

Collaborators:

Taskography - Evaluating robot task planning over large 3D scene graphs

Taskography is the first large-scale robotic task planning benchmark over 3DSGs. While most benchmarking efforts in this area focus on vision-based planning, we systematically study symbolic planning, to decouple planning performance from visual representation learning.

Collaborators:

gradsim

gradSim is a framework that overcomes the dependence on 3D supervision by leveraging differentiable multiphysics simulation and differentiable rendering to jointly model the evolution of scene dynamics and image formation.

Collaborators:
  • Miles Macklin
  • Vikram Voleti
  • Linda Petrini
  • Martin Weiss
  • Jerome Parent-Levesque
  • Kevin Xie
  • Kenny Erleben
  • Florian Shkurti
  • Derek Nowrouzerzahrai
  • Sanja Fidler

gradslam

gradslam is an open-source framework providing differentiable building blocks for simultaneous localization and mapping (SLAM) systems. We enable the usage of dense SLAM subsystems from the comfort of PyTorch.

Collaborators:

La-MAML

Look-ahead meta-learning for continual learning

Collaborators:
  • Karmesh Yadav

Active Domain Randomization

Making sim-to-real transfer more efficient

Collaborators:
  • Chris Pal

Self-supervised visual odometry estimation

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

Collaborators:

Deep Active Localization

Learned active localization, implemented on “real” robots.

Collaborators:
  • Keehong Seo

Department of Computer Science and Operations Research | Université de Montréal | Mila