Bluetooth low energy for autonomous human-robot interaction

© 2017 Copyright held by the owner/author(s).This demonstration shows how inexpensive, off-the-shelf, and unobtrusive Bluetooth Low Energy (BLE) devices can be utilized for enabling robots to recognize touch gestures, to perceive proximity information, and to distinguish between interacting individuals autonomously. The received signal strength (RSS) between the BLE device attached to the robot and BLE devices attached to the interacting individuals is used to achieve this. Almost no software configuration is needed and the setup can be applied to most everyday environments and robot platforms

Autonomous and Intrinsically Motivated Robots for Sustained Human-Robot Interaction

A challenge in using fully autonomous robots in human-robot interaction (HRI) is to design behavior that is engaging enough to encourage voluntary, long-term interaction, yet robust to the perturbations induced by human interaction. It has been repeatedly argued that intrinsic motivations (IMs) are crucial for human development, so it seems reasonable that this mechanism could produce an adaptive and developing robot, which is interesting to interact with. This thesis evaluates whether an intrinsically motivated robot can lead to sustained HRI. Recent research showed that robots which ‘appeared’ intrinsically motivated raised interest in the human interaction partner. The displayed IMs resulted from ‘unpredictably’ asking a question or from a self-disclosing statement. They were designed with the help of pre-defined scripts or teleoperation. An issue here is that this practice renders the behavior less robust toward unexpected input or requires a trained human in the loop. Instead, this thesis proposes a computational model of IM to realize fully autonomous and adaptive behavior generation in a robot. Previous work showed that predictive information maximization leads to playful, exploratory behavior in simulated robots that is robust to changes in the robot’s morphology and environment. This thesis demonstrates how to deploy the formalism on a physical robot that interacts with humans. The thesis conducted three within-subjects studies, where participants interacted with a fully autonomous Sphero BB8 robot with two behavioral regimes: one realizing an adaptive, intrinsically motivated behavior and the other being reactive, but not adaptive. The first study contributes to the idea of the overall proposed study design: the interaction needs to be designed in such a way, that participants are not given any idea of the robot’s task. The second study implements this idea, letting participants focus on answering the question of whether the robots are any different. It further contributes ideas for a more ‘challenging’ baseline behavior motivating the third and final study. Here, a systematic baseline is generated and shows that participants perceive it as almost indistinguishable and similarly animated compared to the intrinsically motivated robot. Despite the emphasis on the design of similarly perceived baseline behaviors, quantitative analyses of post-interaction questionnaires after each study showed a significantly higher perception of the dimension ‘Warmth’ for the intrinsically motivated robot compared to the baseline behavior. Warmth is considered a primary dimension for social attitude formation in social cognition. A human perceived as warm (i.e. friendly and trustworthy) experiences more positive social interactions. The Robotic Social Attribute Scale (RoSAS) implements the scale dimension Warmth for the HRI domain, which has been validated with a series of still images. Going beyond static images, this thesis provides support for the use and applicability of this scale dimension for the purpose of comparing behaviors. It shows that participants prefer to continue interacting with the robot they perceive highest in Warmth. This research opens new research avenues, in particular with respect to different physical robots and longitudinal studies, which are ought to be performed to corroborate the results presented here. However, this thesis shows the general methods presented here, which do not require a human operator in the loop, can be used to imbue robots with behavior leading to positive perception by their human interaction partners, which can yield sustained HRI

Human Perception of Intrinsically Motivated Autonomy in Human-Robot Interaction

Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: MS and DP acknowledge support by the socSMCs FET Proactive project [grant number H2020-641 321], and KD acknowledges funding from the Canada 150 Research Chairs Program. Publisher Copyright: © The Author(s) 2022.A challenge in using robots in human-inhabited environments is to design behavior that is engaging, yet robust to the perturbations induced by human interaction. Our idea is to imbue the robot with intrinsic motivation (IM) so that it can handle new situations and appears as a genuine social other to humans and thus be of more interest to a human interaction partner. Human-robot interaction (HRI) experiments mainly focus on scripted or teleoperated robots, that mimic characteristics such as IM to control isolated behavior factors. This article presents a "robotologist" study design that allows comparing autonomously generated behaviors with each other, and, for the first time, evaluates the human perception of IM-based generated behavior in robots. We conducted a within-subjects user study (N=24) where participants interacted with a fully autonomous Sphero BB8 robot with different behavioral regimes: one realizing an adaptive, intrinsically motivated behavior and the other being reactive, but not adaptive. The robot and its behaviors are intentionally kept minimal to concentrate on the effect induced by IM. A quantitative analysis of post-interaction questionnaires showed a significantly higher perception of the dimension "Warmth" compared to the reactive baseline behavior. Warmth is considered a primary dimension for social attitude formation in human social cognition. A human perceived as warm (friendly, trustworthy) experiences more positive social interactions.Peer reviewedFinal Published versio

The human in the loop Perspectives and challenges for RoboCup 2050

Robotics researchers have been focusing on developing autonomous and human-like intelligent robots that are able to plan, navigate, manipulate objects, and interact with humans in both static and dynamic environments. These capabilities, however, are usually developed for direct interactions with people in controlled environments, and evaluated primarily in terms of human safety. Consequently, human-robot interaction (HRI) in scenarios with no intervention of technical personnel is under-explored. However, in the future, robots will be deployed in unstructured and unsupervised environments where they will be expected to work unsupervised on tasks which require direct interaction with humans and may not necessarily be collaborative. Developing such robots requires comparing the effectiveness and efficiency of similar design approaches and techniques. Yet, issues regarding the reproducibility of results, comparing different approaches between research groups, and creating challenging milestones to measure performance and development over time make this difficult. Here we discuss the international robotics competition called RoboCup as a benchmark for the progress and open challenges in AI and robotics development. The long term goal of RoboCup is developing a robot soccer team that can win against the world’s best human soccer team by 2050. We selected RoboCup because it requires robots to be able to play with and against humans in unstructured environments, such as uneven fields and natural lighting conditions, and it challenges the known accepted dynamics in HRI. Considering the current state of robotics technology, RoboCup’s goal opens up several open research questions to be addressed by roboticists. In this paper, we (a) summarise the current challenges in robotics by using RoboCup development as an evaluation metric, (b) discuss the state-of-the-art approaches to these challenges and how they currently apply to RoboCup, and (c) present a path for future development in the given areas to meet RoboCup’s goal of having robots play soccer against and with humans by 2050.</p

The human in the loop Perspectives and challenges for RoboCup 2050

© 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Robotics researchers have been focusing on developing autonomous and human-like intelligent robots that are able to plan, navigate, manipulate objects, and interact with humans in both static and dynamic environments. These capabilities, however, are usually developed for direct interactions with people in controlled environments, and evaluated primarily in terms of human safety. Consequently, human-robot interaction (HRI) in scenarios with no intervention of technical personnel is under-explored. However, in the future, robots will be deployed in unstructured and unsupervised environments where they will be expected to work unsupervised on tasks which require direct interaction with humans and may not necessarily be collaborative. Developing such robots requires comparing the effectiveness and efficiency of similar design approaches and techniques. Yet, issues regarding the reproducibility of results, comparing different approaches between research groups, and creating challenging milestones to measure performance and development over time make this difficult. Here we discuss the international robotics competition called RoboCup as a benchmark for the progress and open challenges in AI and robotics development. The long term goal of RoboCup is developing a robot soccer team that can win against the world’s best human soccer team by 2050. We selected RoboCup because it requires robots to be able to play with and against humans in unstructured environments, such as uneven fields and natural lighting conditions, and it challenges the known accepted dynamics in HRI. Considering the current state of robotics technology, RoboCup’s goal opens up several open research questions to be addressed by roboticists. In this paper, we (a) summarise the current challenges in robotics by using RoboCup development as an evaluation metric, (b) discuss the state-of-the-art approaches to these challenges and how they currently apply to RoboCup, and (c) present a path for future development in the given areas to meet RoboCup’s goal of having robots play soccer against and with humans by 2050.Peer reviewe

Intrinsically Motivated Autonomy in Human-Robot Interaction: Human Perception of Predictive Information in Robots

© Springer Nature Switzerland AG 2019In this paper we present a fully autonomous and intrinsically motivated robot usable for HRI experiments. We argue that an intrinsically motivated approach based on the Predictive Information formalism, like the one presented here, could provide us with a pathway towards autonomous robot behaviour generation, that is capable of producing behaviour interesting enough for sustaining the interaction with humans and without the need for a human operator in the loop. We present a possible reactive baseline behaviour for comparison for future research. Participants perceive the baseline and the adaptive, intrinsically motivated behaviour differently. In our exploratory study we see evidence that participants perceive an intrinsically motivated robot as less intelligent than the reactive baseline behaviour. We argue that is mostly due to the high adaptation rate chosen and the design of the environment. However, we also see that the adaptive robot is perceived as more warm, a factor which carries more weight in interpersonal interaction than competence

Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A solid methodology to understand human perception and preferences in human-robot interaction (HRI) is crucial in designing real-world HRI. Social cognition posits that the dimensions Warmth and Competence are central and universal dimensions characterizing other humans. The Robotic Social Attribute Scale (RoSAS) proposes items for those dimensions suitable for HRI and validated them in a visual observation study. In this paper we complement the validation by showing the usability of these dimensions in a behavior based, physical HRI study with a fully autonomous robot. We compare the findings with the popular Godspeed dimensions Animacy, Anthropomorphism, Likeability, Perceived Intelligence and Perceived Safety. We found that Warmth and Competence, among all RoSAS and Godspeed dimensions, are the most important predictors for human preferences between different robot behaviors. This predictive power holds even when there is no clear consensus preference or significant factor difference between conditions

Decoding the amplitude and slope of continuous signals into spikes with a spiking point neuron model

© 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/In this study, we harness the signal processing potential of neurons, utilizing the Izhikevich point neuron model to efficiently decode the slope or amplitude of fluctuating continuous input signals. Using biophysically detailed compartmental neurons often requires significant computational resources. We present a novel approach to create behaviours and simulate these interactions in a lower-dimensional space, thereby reducing computational requirements. We began by conducting an extensive search of the Izhikevich parameter space, leading to the first significant outcome of our study: i) the identification of optimal parameter sets for generating slope or amplitude detectors, thereby achieving signal processing goals using neurons. Next, we compared the performance of the slope detector we discovered with a biophysically detailed two-compartmental pyramidal neuron model. Our findings revealed several key observations: ii) bursts primarily occurred on the rising edges of similar input signals, iii) our slope detector exhibited bidirectional slope detection capabilities, iv) variations in burst duration encoded the magnitude of input slopes in a graded manner. Overall, our study demonstrates the efficient and accurate simulation of dendrosomatic behaviours. Real-time applications in robotics or neuromorphic hardware can utilize our approach. While biophysically detailed compartmental neurons are compatible with such hardware, Izhikevich point neurons are more efficient. This work has the potential to facilitate the simulation of such interactions on a larger scale, encompassing a greater number of neurons and neuronal connections for the same computational power