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Robots are envisioned to perform complex tasks in real, noisy, imperfect, partially observable, hardly perceivable and unexpectedly behaving environments. Due to the nature of the physical world, it is crucial to endow robotic systems with the ability to handle uncertainties. Probabilistic robotics, a vibrant field that has gained enormous popularity since its inception in the mid-1990s, provides a compelling paradigm to address this challenge.

The evolution of probabilistic techniques in robotics connects the field to other adjacent research domains such as artificial intelligence (AI). While classical AI still plays an important role in robotic systems, its modern counterpart - empowered by Deep Learning - is able to remarkably enhance the functionality of our robots with its impressive performance on different robotic tasks. However, each coin has two sides. The uninterpretable nature of DL and its data-hungry learning process unfortunately limits the applications of various probabilistic approaches. Though there have been advances in pursuing extension of this concept in recent years, many core challenges associated with robot deployment to the real world still remain unsolved. In consideration of this, it is important to take a look at the probabilistic techniques in the age of pre-DL and peri-DL from a probabilistic robotics point of view.

In light of the above, this workshop aims at providing a forum for robotic and machine learning researchers as well as industrial experts with experience on developing probabilistic methods. The discussions will be centered on past achievements, current requirements, urgent challenges and future research directions and promising applications.

Acknowledgement