Workshop results
Workshop proceedings are available via Program and Speakers.
For a wrap-up of the workshop and discussion, click here (pdf).

This will be the second edition of WIPPAS. Please follow the link to WIPPAS2018 to see the program and proceedings of our first workshop.

Robots rely on models of themselves and the environment to understand and act in the world around them. In many cases, these models are trained on observed (sampled) data, and the goal is to collect the set of data that will generate the most useful model within the resource constraints of the operating robot system. This process is known as adaptive informative sampling, and is applicable to a wide range of robotic applications, from modeling environmental phenomena to approximating value functions in reinforcement learning. However, despite the prevalence of adaptive sampling methods in state-of-the-art robotic applications, there are still many challenging and open problems. For example, how should we:

The main goal of this workshop is to discuss and share ideas related to informative path planning and adaptive sampling. This is a topic that spans all robotic domains and we want to bring together researchers from all fields—marine, ground and aerial robotics, as well as the multi-agent and learning communities—who might otherwise not be aware of the valuable techniques being developed in each of these domains, and the correspondence between their research. This workshop will look at the various aspects of informative path planning, including, but not limited to, its theoretical foundations, active sampling, spatio-temporal variability, multi-robot planning, and its application to real-world problems.

Topics of interest

Important dates

Workshop paper submission deadline: May 22, 2019  May 31, 2019 (AOE)
Notification of acceptance: June 10, 2019
Camera-ready paper: June 15, 2019
Workshop: June 22, 2019 (full day)
Building 101, Floor 02, Room 016/018
Faculty of Engineering

This workshop is endorsed by the IEEE Technical Committee on Multi-Robot Systems, the Marine Robotics Technical Committee, and the Technical Committee for Robot Learning.