prof. Ing. Tomáš Svoboda, Ph.D.

In humans, interaction with the environment is mediated mainly by visual, auditory, and tactile sensations that need to be integrated with information about the current position and posture of the body and prior knowledge about its size and shape. In psychology and the neurosciences the key terms used are body schema, body image, and peripersonal space representations. In robots, multimodal sensing has received comparatively less attention and tactile information has been typically concentrated on the end-point only. With the advent of artificial tactile systems and their application to robots - covering not only the end-effector, but large areas of the body -, it is now possible to study multisensory integration that is necessary for safe interaction of robots with their surroundings, including humans. The goal of this work is to develop or newly apply machine learning methods (such as Restricted Boltzmann Machine) that will warrant Bayes optimal behavior of robots in complex environments, in which physical contact of different body parts with other objects is unavoidable. https://sites.google.com/site/matejhof/research/body-schema

Multimodal data like RGB images, depth images, thermal images, Lidar measurements are vital for autonomous systems like robots or cars. We aim at combining various modalities optimally, with a particular interest in possibly missing data. For machine learning methods we use, it is necessary to create large realistic dataset in an automated way. Many simulators exist however, generating realistic multimodal data is still a research challenge we want to address.