47,493 research outputs found

    Theory of mind and information relevance in human centric human robot cooperation

    Get PDF
    In the interaction with others, besides consideration of environment and task requirements, it is crucial to account for and develop an understanding for the interaction partner and her state of mind. An understanding of other’s state of knowledge and plans is important to support efficient interaction activities including information sharing, or distribution of sub- tasks. A robot cooperating with and supporting a human partner might decide to communicate information that it has collected. However, sharing every piece of information is not feasible, as not all information is both, currently relevant and new for the human partner, but instead will annoy and dis- tract her from other important activities. An understanding for the human state of mind will enable the robot to balance communication according to the needs of the human partner and the efforts of communication for both. An artificial theory of mind is proposed as Bayesian inference of human beliefs during interaction. It relies on a general model for human information perception and decision making. To cope with the complexity of second order inference – estimating what the human inferred of her environment – an efficient linearization based filtering approach is introduced. The inferred human belief, as understanding of her mental state, is used to estimate her situation awareness. When this is missing, e.g. the human is unaware of some important piece of information, the robot provides supportive communication. It therefore evaluates relevance and novelty of information compared to communication efforts following a systematic information sharing concept. The robot decides about whether, when and what type of information it should provide in a current situation to sup- port the human efficiently without annoying. The decision is derived by planning under uncertainty while considering the inferred human belief in relation to the task requirements. Systematic properties and benefits of the derived concepts are discussed in illustrative example situations. Two human robot collaborative tasks and corresponding user studies were designed and investigated, applying artificial theory of mind as be- lief inference and assistive communication in the interaction with humans. Equipped with the artificial theory of mind, the robot is able to infer in- terpretable information about the human’s mental state and can detect a lack of human awareness. Supported by adaptive human centric information sharing, participants could recover much earlier from unawareness. A comparison to state-of-the-art communication strategies demonstrates the efficiency, as the new concept explicitly balances benefits and costs of communication to support while avoiding unnecessary interruptions. By sharing information according to human needs and environmental urgency, the robot does not take over nor instruct the human, but enables her to make good decisions herself

    Engineering Social Learning Mechanisms for Multi-Agent Interaction

    Get PDF
    This thesis is strongly inspired by literature on animal social learning, applying it to multi-robot as well as human-robot interaction scenarios, Social learning, which can include complex or simple social mechanisms, allow us to understand cooperation and communication in animals, giving them better chances to survive for longer and thrive as a society. For this dissertation, to translate this understanding into socially rich behavior among multi-agent robots and Human-Robot Interaction, two experiments were conducted. The first experiment focused on how social learning might optimize cooperation among robots (in a robot 'society') for the problem of foraging. The task utilizes small and simple swarm robots to understand how such social mechanisms might play a role in establishing rules for emergent group behavior and how social rules might be engineered to gain useful effects in a group of robots. The study investigated exploratory behavior without interaction (asocial) and with interaction (social). The results from this exploratory study suggest that deterministic asocial exploration is best performed by a Spiral exploration mechanisms. However, these asocial exploration strategies are eclipsed by certain types of social reward sharing strategies as long as sharing occurs for at least half the lifetime of the robots. Sharing locations of reward caches for all time is of course the most optimal, but comes at the cost of communicating longer and hence using more energy both on the sender and receiver’s end. An analysis of a compromise strategy between completely asocial exploration and social reward location sharing is performed using strategies termed critical and conditional learning. It is found that the number of reward caches located through critical and conditional learning are intermediary to the two extremes, namely completely asocial and completely social foraging. The second experiment sought to understand if and how other types of social learning mechanisms such as observational conditioning can facilitate social information spread to human participants. The question of whether, and to what extent, a robot can influence a human's actions is asked through a study designed to understand if emotions displayed by a robot demonstrators can influence human observers. An immersive first-person gaming experience utilizing Unity was designed where a robot demonstrator reacted either positively or negatively to an external stimulus. Objective (position of player in-game) and subjective (Questionnaire) data collected on the human participants' reactions suggests that the virtual robot agent is successful in socially transmitting information. Through these studies, I seek to contribute to the understanding of the role simple social learning mechanisms can play in information transfer among human and robot agents, and to identify useful metrics for the detection of such social mechanisms

    RoboChain: A Secure Data-Sharing Framework for Human-Robot Interaction

    Full text link
    Robots have potential to revolutionize the way we interact with the world around us. One of their largest potentials is in the domain of mobile health where they can be used to facilitate clinical interventions. However, to accomplish this, robots need to have access to our private data in order to learn from these data and improve their interaction capabilities. Furthermore, to enhance this learning process, the knowledge sharing among multiple robot units is the natural step forward. However, to date, there is no well-established framework which allows for such data sharing while preserving the privacy of the users (e.g., the hospital patients). To this end, we introduce RoboChain - the first learning framework for secure, decentralized and computationally efficient data and model sharing among multiple robot units installed at multiple sites (e.g., hospitals). RoboChain builds upon and combines the latest advances in open data access and blockchain technologies, as well as machine learning. We illustrate this framework using the example of a clinical intervention conducted in a private network of hospitals. Specifically, we lay down the system architecture that allows multiple robot units, conducting the interventions at different hospitals, to perform efficient learning without compromising the data privacy.Comment: 7 pages, 6 figure

    Affordance-map : learning hidden human context in 3D scenes through virtual human models

    Full text link
    University of Technology Sydney. Faculty of Engineering and Information Technology.Ability to learn human context in an environment could be one of the most desired fundamental abilities that a robot should possess when sharing workspaces with human co-workers. Arguably, a robot with appropriate human context awareness could lead to a better human robot interaction. This thesis addresses the problem of learning human context in indoor environments by only looking at geometrics features of the environment. The novelty of this concept is, it does not require to observe real humans to learn human context. Instead, it uses virtual human models and their relationships with the environment to map hidden human affordances in 3D scenes. The problem of affordance mapping is formulated as a multi label classification problem with a binary classifier for each affordance type. The initial experiments proved that the SVM classifier is ideally suited for affordance mapping. However, SVM classifier recorded sub-optimum results when trained with imbalanced datasets. This imbalance occurs because in all 3D scenes in the dataset, the number of negative examples outnumbered positive examples by a great margin. As a solution to this, a number of SVM learners that are designed to tolerate class imbalance problem are tested for learning the affordance-map. These algorithms showed some tolerance to moderate class imbalances, but failed to perform well in some affordance types. To mitigate these drawbacks, this thesis proposes the use of Structured SVM (S-SVM) optimized for F1-score. This approach defines the affordance-map building problems as a structured learning problem and outputs the most optimum affordance-map for a given set of features (3D-Images). In addition, S-SVM can be learned efficiently even on a large extremely imbalanced dataset. Further, experimental results of the S-SVM method outperformed previously used classifiers for mapping affordances. Finally, this thesis presents two applications of the affordance-map. In the first application, affordance-map is used by a mobile robot to actively search for computer monitors in an office environment. The orientation and location information of humans models inferred by the affordance-map is used in this application to predict probable locations of computer monitors. The experimental results in a large office environment proved that the affordance-map concept simplifies the search strategy of the robot. In the second application, affordance-map is used for context aware path planning. In this application, human context information of the affordance-map is used by a service robot to plan paths with minimal distractions to office workers

    Modulating interaction times in an artificial society of robots

    Get PDF
    In a collaborative society, sharing information is advantageous for each individual as well as for the whole community. Maximizing the number of agent-to-agent interactions per time becomes an appealing behavior due to fast information spreading that maximizes the overall amount of shared information. However, if malicious agents are part of society, then the risk of interacting with one of them increases with an increasing number of interactions. In this paper, we investigate the roles of interaction rates and times (aka edge life) in artificial societies of simulated robot swarms. We adapt their social networks to form proper trust sub-networks and to contain attackers. Instead of sophisticated algorithms to build and administrate trust networks, we focus on simple control algorithms that locally adapt interaction times by changing only the robots' motion patterns. We successfully validate these algorithms in collective decision-making showing improved time to convergence and energy-efficient motion patterns, besides impeding the spread of undesired opinions
    • …
    corecore