Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of Multimodal Features in Spoken Human-Robot Interaction

Recognition of social signals, from human facial expressions or prosody of speech, is a popular research topic in human-robot interaction studies. There is also a long line of research in the spoken dialogue community that investigates user satisfaction in relation to dialogue characteristics. However, very little research relates a combination of multimodal social signals and language features detected during spoken face-to-face human-robot interaction to the resulting user perception of a robot. In this paper we show how different emotional facial expressions of human users, in combination with prosodic characteristics of human speech and features of human-robot dialogue, correlate with users' impressions of the robot after a conversation. We find that happiness in the user's recognised facial expression strongly correlates with likeability of a robot, while dialogue-related features (such as number of human turns or number of sentences per robot utterance) correlate with perceiving a robot as intelligent. In addition, we show that facial expression, emotional features, and prosody are better predictors of human ratings related to perceived robot likeability and anthropomorphism, while linguistic and non-linguistic features more often predict perceived robot intelligence and interpretability. As such, these characteristics may in future be used as an online reward signal for in-situ Reinforcement Learning based adaptive human-robot dialogue systems.Comment: Robo-NLP workshop at ACL 2017. 9 pages, 5 figures, 6 table

Make me a sandwich! Intrinsic human identification from their course of action

In order to allow humans and robots to work closely together and as a team, we need to equip robots not only with a general understanding of joint action, but also with an understanding of the idiosyncratic differences in the ways humans perform certain tasks. This will allow robots to be better colleagues, by anticipating an individual's actions, and acting accordingly. In this paper, we present a way of encoding a human's course of action as a probabilistic sequence of qualitative states, and show that such a model can be employed to identify individual humans from their respective course of action, even when accomplishing the very same goal state. We conclude from our findings that there are significant variations in the ways humans accomplish the very same task, and that our representation could in future work inform robot (task) planning in collaborative settings

Social distance augmented qualitative trajectory calculus for human-robot spatial interaction

In this paper we propose to augment a wellestablished Qualitative Trajectory Calculus (QTC) by incorporating social distances into the model to facilitate a richer and more powerful representation of Human-Robot Spatial Interaction (HRSI). By combining two variants of QTC that implement different resolutions and switching between them based on distance thresholds we show that we are able to both reduce the complexity of the representation and at the same time enrich QTC with one of the core HRSI concepts: proxemics. Building on this novel integrated QTC model, we propose to represent the joint spatial behaviour of a human and a robot employing a probabilistic representation based on Hidden Markov Models. We show the appropriateness of our approach by encoding different HRSI behaviours observed in a human-robot interaction study and show how the models can be used to represent and classify these behaviours using social distance-augmented QTC

Spectral analysis for long-term robotic mapping

This paper presents a new approach to mobile robot mapping in long-term scenarios. So far, the environment models used in mobile robotics have been tailored to capture static scenes and dealt with the environment changes by means of ‘memory decay’. While these models keep up with slowly changing environments, their utilization in dynamic, real world environments is difficult. The representation proposed in this paper models the environment’s spatio-temporal dynamics by its frequency spectrum. The spectral representation of the time domain allows to identify, analyse and remember regularly occurring environment processes in a computationally efficient way. Knowledge of the periodicity of the different environment processes constitutes the model predictive capabilities, which are especially useful for long-term mobile robotics scenarios. In the experiments presented, the proposed approach is applied to data collected by a mobile robot patrolling an indoor environment over a period of one week. Three scenarios are investigated, including intruder detection and 4D mapping. The results indicate that the proposed method allows to represent arbitrary timescales with constant (and low) memory requirements, achieving compression rates up to 106 . Moreover, the representation allows for prediction of future environment’s state with ∼ 90% precision

Human-robot spatial interaction using probabilistic qualitative representations

Current human-aware navigation approaches use a predominantly metric representation of the interaction which makes them susceptible to changes in the environment. In order to accomplish reliable navigation in ever-changing human populated environments, the presented work aims to abstract from the underlying metric representation by using Qualitative Spatial Relations (QSR), namely the Qualitative Trajectory Calculus (QTC), for Human-Robot Spatial Interaction (HRSI). So far, this form of representing HRSI has been used to analyse different types of interactions online. This work extends this representation to be able to classify the interaction type online using incrementally updated QTC state chains, create a belief about the state of the world, and transform this high-level descriptor into low-level movement commands. By using QSRs the system becomes invariant to change in the environment, which is essential for any form of long-term deployment of a robot, but most importantly also allows the transfer of knowledge between similar encounters in different environments to facilitate interaction learning. To create a robust qualitative representation of the interaction, the essence of the movement of the human in relation to the robot and vice-versa is encoded in two new variants of QTC especially designed for HRSI and evaluated in several user studies. To enable interaction learning and facilitate reasoning, they are employed in a probabilistic framework using Hidden Markov Models (HMMs) for online classiffication and evaluation of their appropriateness for the task of human-aware navigation. In order to create a system for an autonomous robot, a perception pipeline for the detection and tracking of humans in the vicinity of the robot is described which serves as an enabling technology to create incrementally updated QTC state chains in real-time using the robot's sensors. Using this framework, the abstraction and generalisability of the QTC based framework is tested by using data from a different study for the classiffication of automatically generated state chains which shows the benefits of using such a highlevel description language. The detriment of using qualitative states to encode interaction is 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 the sampling space of a reactive local planner to only allow the generation of trajectories that correspond to the desired QTC state. This results in a exible and agile behaviour I generation that is able to produce inherently safe paths. In order to classify the current interaction type online and predict the current state for action selection, the HMMs are evolved into a particle filter especially designed to work with QSRs of any kind. This online belief generation is the basis for a exible action selection process that is based on data acquired using Learning from Demonstration (LfD) to encode human judgement into the used model. Thereby, the generated behaviour is not only sociable but also legible and ensures a high experienced comfort as shown in the experiments conducted. LfD itself is a rather underused approach when it comes to human-aware navigation but is facilitated by the qualitative model and allows exploitation of expert knowledge for model generation. Hence, the presented work bridges the gap between the speed and exibility of 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 expert knowledge about the unfolding of an interaction

Real-time multisensor people tracking for human-robot spatial interaction

All currently used mobile robot platforms are able to navigate safely through their environment, avoiding static and dynamic obstacles. However, in human populated environments mere obstacle avoidance is not sufficient to make humans feel comfortable and safe around robots. To this end, a large community is currently producing human-aware navigation approaches to create a more socially acceptable robot behaviour. Amajorbuilding block for all Human-Robot Spatial Interaction is the ability of detecting and tracking humans in the vicinity of the robot. We present a fully integrated people perception framework, designed to run in real-time on a mobile robot. This framework employs detectors based on laser and RGB-D data and a tracking approach able to fuse multiple detectors using different versions of data association and Kalman filtering. The resulting trajectories are transformed into Qualitative Spatial Relations based on a Qualitative Trajectory Calculus, to learn and classify different encounters using a Hidden Markov Model based representation. We present this perception pipeline, which is fully implemented into the Robot Operating System (ROS), in a small proof of concept experiment. All components are readily available for download, and free to use under the MIT license, to researchers in all fields, especially focussing on social interaction learning by providing different kinds of output, i.e. Qualitative Relations and trajectories

Navigation testing for continuous integration in robotics

Robots working in real-world applications need to be robust and reliable. However, ensuring robust software in an academic development environment with dozens of developers poses a significant challenge. This work presents a testing framework, successfully employed in a large-scale integrated robotics project, based on continuous integration and the fork-and-pull model of software development, implementing automated system regression testing for robot navigation. It presents a framework suitable for both regression testing and also providing processes for parameter optimisation and benchmarking

A probabilistic model of human-robot spatial interaction using a qualitative trajectory calculus

In this paper we propose a probabilistic model for Human-Robot Spatial Interaction (HRSI) using a Qualitative Trajectory Calculus (QTC). In particular, we will build on previous work representing HRSI as a Markov chain of QTC states and evolve this to an approach using a Hidden Markov Model representation. Our model accounts for the invalidity of certain transitions within the QTC to reduce the complexity of the probabilistic model and to ensure state sequences in accordance to this representational framework. We show the appropriateness of our approach by using the probabilistic model to encode different HRSI behaviours observed in a human-robot interaction study and show how the models can be used to classify these behaviours reliably. Copyright © 2014, Association for the Advancement of Artificial Intelligence. All rights reserved

Patterns of use: how older adults with progressed dementia interact with a robot

Older adults represent a new user group of robots that are deployed in their private homes or in care facilities. In the presented study tangible aspects of older adults' interaction with an autonomous robot were focused. The robot was deployed as a companion in physical therapy for older adults with progressed dementia. Interaction was possible via a mounted touch screen. The menu was structured in a single layer and icons were big and with strong contrast. Employing a detailed observation protocol, interaction frequencies and contexts were assessed. Thereby, it was found that most of the interaction was encouraged by the therapists and that two out of 12 older adults with progressed dementia showed self-inducted interactions