Human-robot spatial interaction using probabilistic qualitative representations
Creators: name: Dondrup, Christian
Publisher: name: University of Lincoln
Year: 2023
Subjects: H670 - Robotics &amp; cybernetics
I140 - Human-computer interaction

description: Current human-aware navigation approaches use a predominantly metric representationof the interaction which makes them susceptible to changes in the environment. In orderto accomplish reliable navigation in ever-changing human populated environments, thepresented work aims to abstract from the underlying metric representation by using QualitativeSpatial Relations (QSR), namely the Qualitative Trajectory Calculus (QTC), forHuman-Robot Spatial Interaction (HRSI). So far, this form of representing HRSI has beenused to analyse different types of interactions online. This work extends this representationto be able to classify the interaction type online using incrementally updated QTCstate chains, create a belief about the state of the world, and transform this high-leveldescriptor into low-level movement commands. By using QSRs the system becomes invariantto change in the environment, which is essential for any form of long-term deploymentof a robot, but most importantly also allows the transfer of knowledge between similarencounters in different environments to facilitate interaction learning. To create a robustqualitative representation of the interaction, the essence of the movement of the human inrelation to the robot and vice-versa is encoded in two new variants of QTC especially designedfor HRSI and evaluated in several user studies. To enable interaction learning andfacilitate reasoning, they are employed in a probabilistic framework using Hidden MarkovModels (HMMs) for online classiffication and evaluation of their appropriateness for thetask of human-aware navigation.In order to create a system for an autonomous robot, a perception pipeline for thedetection and tracking of humans in the vicinity of the robot is described which servesas an enabling technology to create incrementally updated QTC state chains in real-timeusing the robot's sensors. Using this framework, the abstraction and generalisability of theQTC based framework is tested by using data from a different study for the classifficationof automatically generated state chains which shows the benefits of using such a highleveldescription language. The detriment of using qualitative states to encode interactionis the severe loss of information that would be necessary to generate behaviour from it.To overcome this issue, so-called Velocity Costmaps are introduced which restrict thesampling space of a reactive local planner to only allow the generation of trajectoriesthat correspond to the desired QTC state. This results in a exible and agile behaviourIgeneration that is able to produce inherently safe paths. In order to classify the currentinteraction type online and predict the current state for action selection, the HMMs areevolved into a particle filter especially designed to work with QSRs of any kind. Thisonline belief generation is the basis for a exible action selection process that is based ondata acquired using Learning from Demonstration (LfD) to encode human judgement intothe used model. Thereby, the generated behaviour is not only sociable but also legibleand ensures a high experienced comfort as shown in the experiments conducted. LfDitself is a rather underused approach when it comes to human-aware navigation but isfacilitated by the qualitative model and allows exploitation of expert knowledge for modelgeneration. Hence, the presented work bridges the gap between the speed and exibilityof a sampling based reactive approach by using the particle filter and fast action selection,and the legibility of deliberative planners by using high-level information based on expertknowledge about the unfolding of an interaction.