Browsing by Subject "Human-Robot Interaction"
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Item Body Pose Predictions in Triadic Social Interactions(2021-05) Girdhar, RishabHuman beings are social animals in that they need to socialize with each other to build companionship and thrive alongside other humans. One of the primary characteristics of social interactions is the signals used by people to communicate their thoughts effectively. These include gesturing with their hands, moving around etc.. AI agents or algorithms interacting with humans which we refer to as Social artificial intelligence must learn to interpret and predict these signals in order to use them to interact with other humans successfully. Data-driven approaches have helped make remarkable strides in many artificial intelligence tasks and could similarly help machines learn the body gestures of interacting individuals. We define a framework for predicting these gestures in a triadic social interactions scenario where the humans play a game of haggling and two sellers try to sell their products to a buyer.Item De-noising Motion Predictions of Scuba Divers for Aquatic Robots(2021)Current diver predictors output a sequence of bounding boxes, with two corners randomly sampled from two bivariate gaussians. This introduces noise and uncertainty into the prediction outputs and makes the conversion of this sequence of 2D boxes into a 3D motion vector challenging. This poster describes an approach to de-noise this output and convert the predictions into a format that can be used by aquatic robots to plan their motion and follow scuba divers robustly.Item An Empirical Study of Communication-Free Coordination in Human-Robot Teams Through a Coverage Control Game(2020-05) Kuan, Jin Hong; Yazıcıoğlu, A. Yasin; Aksaray, DeryaWe investigate the performance in coverage control problems, where some robots are controlled by human operators and there are no explicit communications among the robots for coordination. One example of such a scenario is a team of unmanned and manned vehicles together pursuing a surveillance mission, where each vehicle operates based on local observations without communicating with others due to physical or strategic limitations. For such scenarios, there exist distributed algorithms that ensure (near-) optimal long-run average performance when followed by all robots. This paper is focused on how the team performance changes when some robots are controlled by human operators rather than following such an optimal algorithm. For the empirical analysis, we have designed a multi-player computer game, where each player (human operator) controls a single robot and the autonomous robots follow a noisy greedy algorithm to optimize their marginal contribution to the overall coverage. We present the results obtained on multiple maps with a team of four robots, where the number of players range from zero (all robots are autonomous) to four (each robot is controlled by a player). Our results indicate that long-run average performance degrades with the introduction of human players, but this effect is not always monotonous with respect to the number of human players. Furthermore, through post-test questionnaires we showed that performance is a good predictor of the outcome in human subjective assessments. On the other hand, the number of human players in a team was not found to have any significant effect on subjective assessment.Item Humanoid Robot - Human Interaction: Towards Compliance and Reciprocity with a Social Robot Through Completion of a Pregiving Favor(2023-09) Moberg, ReillyUnderstanding the social and natural relationships that humans have with ad-vanced technology is an extremely important consideration in the design and develop- ment of humanoid social robots. By perceiving the social rules within human-human interaction and applying them to human-robot interaction, social influence can lead to participants being more willing and eager to interact with a robot, resulting in the robot being used to its full potential. By combining the work done by Reeves and Nass, 2006 studying the media equa- tion with the social rule of reciprocity (Cialdini, 2008), we suggest that when a robot completes a pregiving favor for a human participant, then the human participant will be influenced by the social rule of reciprocation to comply by the robot’s later request. A phasic, between-subjects experiment (N = 72) using facial electromyography (zygomatic and corrugator) was conducted to learn more about how the natural, hu- man behavior of reciprocation can be applied to human-robot interaction. Measured in this study is the user’s valence of emotions, the user’s willingness to reciprocate a favor, and the measure of compliance based on the number of raffle tickets purchased by the user at the robot’s request. The results suggest that the social rule of recipro- cation exists within human-robot interaction and that when a robot offers a pregiving favor to a person, then that person is more likely to comply with the robot’s later request. In concluding, we discuss theoretical contributions, limitations, and avenues for future research.Item Using LED Gaze Cues to Enhance Underwater Human-Robot Interaction(2022-05) Prabhu, Aditya; Fulton, Michael; Sattar, Junaed, Ph.D.In the underwater domain, conventional methods of communication between divers and Autonomous Underwater Vehicles (AUVs) are heavily impeded. Radio signal attenuation, water turbidity (cloudiness), and low light levels make it difficult for a diver and AUV to relay information between each other. Current solutions such as underwater tablets, slates, and tags are not intuitive and introduce additional logistical challenges and points of failure. Intuitive human-robot interaction (HRI) is imperative to ensuring seamless collaboration between AUVs and divers. Eye gazes are a natural form of relaying information between humans, and are an underutilized channel of communication in AUVs, while lights help eliminate concerns of darkness, turbidity, and signal attenuation which often impair diver-robot collaboration. This research aims to implement eye gazes on LoCO (a low-cost AUV) using RGB LED rings in order to pursue intuitive forms of HRI underwater while overcoming common barriers to communication. To test the intuitiveness of the design, 9 participants with no prior knowledge of LoCO and HRI were tasked with recalling the meanings for each of 16 gaze indicators during pool trials, while being exposed to the indicators 3 to 4 days earlier. Compared to the baseline text display communication, which had a recall of 100%, the recall for most eye gaze animations were exceptionally high, with an 80% accuracy score for 11 of the 16 indicators. These results suggest certain eye indicators convey information more intuitively than others, and additional training can make gaze indicators a viable method of communication between humans and robots.