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Browsing by Subject "Human-Robot Interaction"

Now showing 1 - 7 of 7
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    Body Pose Predictions in Triadic Social Interactions
    (2021-05) Girdhar, Rishab
    Human 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.
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    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.
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    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, Derya
    We 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.
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    Humanoid Robot - Human Interaction: Towards Compliance and Reciprocity with a Social Robot Through Completion of a Pregiving Favor
    (2023-09) Moberg, Reilly
    Understanding 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.
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    Toward Visual Communication Methods for Underwater Human-Robot Interaction
    (2024-04) Edge, Chelsey
    Trained divers take on the complex and often dangerous underwater environment to perform essential tasks. These tasks include inspection and repair of underwater infrastructure and monitoring the health of water systems through tasks such as observations of coral reefs and tracking of invasive species. Autonomous Underwater Vehicles (AUVs) able to assist with these tasks have become more widely deployed as their capabilities improve, however, when deployed as solo agents they lack the intuition and ability to adapt to unexpected situations as a human diver would. The objective of collaboration between a diver and an AUV brings together the ability of an AUV to perform tasks that are dangerous to the human diver, while maintaining the ability of the diver to monitor the situation and update task information as necessary. For this collaboration to be successful meaningful communication is essential, especially when the goal of the collaboration is to complete a task. This dissertation presents our work towards improving diver-AUV collaboration, focusing on utilizing visual perception onboard the AUV. In the following chapters, we discuss two novel communication algorithms that allow divers to communicate information about the location of an object required by an AUV to perform a task. These methods have been designed to take into account challenges such as limitations of on-board computation as well as challenges inherent to working in the underwater domain, such as non-traditional human body poses and limitations of traditional, terrestrial, computer vision. Evaluations of these methods are performed onboard AUVs. We then incorporate these algorithms into a communication system which allows a diver to assign the AUV a task based on the object detected. This system also provides feedback from the AUV to the diver about the task which will be performed, forming a closed loop communication system between diver and AUV. Validation of this system was performed fully onboard an AUV in the Caribbean Sea. In addition, as AUV visual perception can be hampered by the visual degradation of the underwater environment, we therefore present an investigation into a task-based method to improve AUV vision. We also discuss our contributions to the design and creation of the research platforms necessary for this research to move forward.
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    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.
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    Vision-Based Computational Methods Towards Effective Underwater Multi-Human-Robot Interaction
    (2024-05) Enan, Sadman Sakib
    Numerous important tasks, such as environmental monitoring, cable or wreckage inspection, search-and-rescue, and oil drilling or spillage monitoring, are conducted underwater. A team of human divers typically carries out these challenging and often dangerous tasks, occasionally receiving assistance from a Remotely Operated Vehicle (ROV). However, an ROV is mainly controlled by someone on the surface which leads to inefficient collaboration due to the indirect engagement among the divers and the robot. In contrast, an Autonomous Underwater Vehicle (AUV) does not require a surface operator to operate and can significantly enhance task efficiency by actively engaging with a team of human divers and other AUVs during the tasks. Thus, it is imperative for the AUVs to have robust multi-Human-Robot Interaction (mHRI) capability. In this dissertation, we present a set of vision-based computational methods for AUV perception to facilitate effective underwater mHRI to allow successful collaboration among multiple divers and AUVs. Furthermore, we provide several novel underwater datasets designed to facilitate learning about robot motion, diver identity, their pose information, and multi-human-robot collaborative scenarios. Our proposed methods allow the AUV to enable human-comprehensible interaction between multiple AUVs, identify unique divers for secure interaction and collaboration, reposition itself for interaction by determining whether their human partners are attentive, and identify the current activity of divers to make informed decisions. However, the general operation of AUVs is severely impacted by various factors, such as water turbidity, rapid currents, varying lighting conditions, and signal attenuation. AUVs also have several platform-specific constraints, such as finite battery life, limited on-board processing power, and real-time operational requirements. We have taken these challenges into consideration while designing and implementing our proposed algorithms on-board physical AUV platforms. We have elaborated on the rationale behind the specific design choices made for each system. Experimentalvalidations on proposed datasets as well as through numerous robot trials, performed in both closed- and open-water environments (e.g., swimming pools and oceans), show the efficacy of each proposed system.