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Register a new userIf it Looks like a Human and Speaks like a Human ... Dialogue and cooperation in human-robot interactions
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The paper presents the results of a behavioral experiment conducted between February 2020 and March 2021 at Universit`a Cattolica del Sacro Cuore, Milan Campus in which students were matched to either a human or a humanoid robotic partner to play an iterated Prisoners Dilemma. The results of a Logit estimation procedure show that subjects are more likely to cooperate with human rather robotic partners; that are more likely to cooperate after receiving a dialogic verbal reaction following the realization of a sub-obtimal social outcome; that the effect of the verbal reaction is independent on the nature of the partner. Our findings provide new evidence on the effect of verbal communication in strategic frameworks. Results are robust to the exclusion of students of Economics related subjects, to the inclusion of a set of psychological and behavioral controls, to the way subjects perceive robots behavior and to potential gender biases in human-human interactions.
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In this paper we propose FlexHRC+, a hierarchical human-robot cooperation architecture designed to provide collaborative robots with an extended degree of autonomy when supporting human operators in high-variability shop-floor tasks. The architecture encompasses three levels, namely for perception, representation, and action. Building up on previous work, here we focus on (i) an in-the-loop decision making process for the operations of collaborative robots coping with the variability of actions carried out by human operators, and (ii) the representation level, integrating a hierarchical AND/OR graph whose online behaviour is formally specified using First Order Logic. The architecture is accompanied by experiments including collaborative furniture assembly and object positioning tasks.
We describe a multi-phased Wizard-of-Oz approach to collecting human-robot dialogue in a collaborative search and navigation task. The data is being used to train an initial automated robot dialogue system to support collaborative exploration tasks. In the first phase, a wizard freely typed robot utterances to human participants. For the second phase, this data was used to design a GUI that includes buttons for the most common communications, and templates for communications with varying parameters. Comparison of the data gathered in these phases show that the GUI enabled a faster pace of dialogue while still maintaining high coverage of suitable responses, enabling more efficient targeted data collection, and improvements in natural language understanding using GUI-collected data. As a promising first step towards interactive learning, this work shows that our approach enables the collection of useful training data for navigation-based HRI tasks.
Personality has been identified as a vital factor in understanding the quality of human robot interactions. Despite this the research in this area remains fragmented and lacks a coherent framework. This makes it difficult to understand what we know and identify what we do not. As a result our knowledge of personality in human robot interactions has not kept pace with the deployment of robots in organizations or in our broader society. To address this shortcoming, this paper reviews 83 articles and 84 separate studies to assess the current state of human robot personality research. This review: (1) highlights major thematic research areas, (2) identifies gaps in the literature, (3) derives and presents major conclusions from the literature and (4) offers guidance for future research.
Since the late 1990s when speech companies began providing their customer-service software in the market, people have gotten used to speaking to machines. As people interact more often with voice and gesture controlled machines, they expect the machines to recognize different emotions, and understand other high level communication features such as humor, sarcasm and intention. In order to make such communication possible, the machines need an empathy module in them which can extract emotions from human speech and behavior and can decide the correct response of the robot. Although research on empathetic robots is still in the early stage, we described our approach using signal processing techniques, sentiment analysis and machine learning algorithms to make robots that can understand human emotion. We propose Zara the Supergirl as a prototype system of empathetic robots. It is a software based virtual android, with an animated cartoon character to present itself on the screen. She will get smarter and more empathetic through its deep learning algorithms, and by gathering more data and learning from it. In this paper, we present our work so far in the areas of deep learning of emotion and sentiment recognition, as well as humor recognition. We hope to explore the future direction of android development and how it can help improve peoples lives.
A playtest is the process in which human testers are recruited to play video games and to reveal software bugs. Manual testing is expensive and time-consuming, especially when there are many mobile games to test and every software version requires for extensive testing before being released. Existing testing frameworks (e.g., Android Monkey) are limited because they adopt no domain knowledge to play games. Learning-based tools (e.g., Wuji) involve a huge amount of training data and computation before testing any game. This paper presents LIT -- our lightweight approach to generalize playtesting tactics from manual testing, and to adopt the generalized tactics to automate game testing. LIT consists of two phases. In Phase I, while a human plays an Android game app G for a short period of time (e.g., eight minutes), tool records the users actions (e.g., swipe) and the scene before each action. Based on the collected data, LIT generalizes a set of emph{context-aware, abstract playtesting tactics} which describe under what circumstances, what actions can be taken to play the game. In Phase II, LIT tests G based on the generalized tactics. Namely, given a randomly generated game scene, LIT searches match for the abstract context of any inferred tactic; if there is a match, LIT customizes the tactic and generates a feasible event to play the game. Our evaluation with nine games shows LIT to outperform two state-of-the-art tools. This implies that by automating playtest, LIT will significantly reduce manual testing and boost the quality of game apps.
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