In this workshop we will discuss the importance of uncertainty in deep learning for robotic applications. In addition, the workshop will host the 2nd Probabilistic Object Detection challenge, a new challenge that evaluates the ability of visual object detectors to accurately quantify their spatial and semantic uncertainty.

The workshop will provide tutorial-style talks that cover the state-of-the-art of uncertainty quantification in deep learning, specifically Bayesian and non-Bayesian approaches, spanning perception, world-modeling, decision making, and actions. Invited expert speakers will discuss the importance of uncertainty in deep learning for robotic perception, but also action. In addition the workshop will provide a forum to discuss novel and ongoing work in a variety of topics:

Topics of Interest


The workshop will also host the 2nd Probabilistic Object Detection challenge. This is the first object detection challenge requiring a probabilistic aspect for detection: A novel metric evaluates both the spatial and semantic uncertainty of the object detector. Providing reliable uncertainty information is essential for robotics applications where actions triggered by erroneous but high-confidence perception can lead to catastrophic results.

Important Dates

to be announced

Invited Speakers

to be announced


to be announced


The Robotic Vision Challenges organisers are with the Australian Centre for Robotic Vision and Google AI.

Niko Sünderhauf
Queensland University of Technology
Feras Dayoub
Queensland University of Technology
Dimity Miller
Queensland University of Technology
Anelia Angelova
Google Brain
David Hall
Queensland University of Technology
Haoyang Zhang
Queensland University of Technology
Gustavo Carneiro
University of Adelaide
Tom Drummond
Monash University