B.E.S.T: Basic Emotion & Sentiment Tracking

The emergence of depression, personality disorders, serious emotional disturbance & social anxiety disorder among children in our modern society is now alarming. Lack of interaction & communication is a major aspect of it. Nowadays, in nuclear family children aren’t able to get much of interaction with elders which leaves them most of the time by themselves, this affects their emotional development & expressiveness. So, our primary focus is on development of a system which can be fitted into any preferable or favourite toy which will ease the process of developing a child-toy emotional bond. Later the same will help for tracking of emotion & sentiments as the relation which child develops with the companion toy helps the child to be expressive about his/her thoughts, feelings & day to day events. A subsidiary goal is to improve communication & interaction between child and parents

Personalizing human-agent interaction through cognitive models

Cognitive modeling of human behavior has advanced the understanding of underlying processes in several domains of psychology and cognitive science. In this article, we outline how we expect cognitive modeling to improve comprehension of individual cognitive processes in human-agent interaction and, particularly, human-robot interaction (HRI). We argue that cognitive models offer advantages compared to data-analytical models, specifically for research questions with expressed interest in theories of cognitive functions. However, the implementation of cognitive models is arguably more complex than common statistical procedures. Additionally, cognitive modeling paradigms typically have an explicit commitment to an underlying computational theory. We propose a conceptual framework for designing cognitive models that aims to identify whether the use of cognitive modeling is applicable to a given research question. The framework consists of five external and internal aspects related to the modeling process: research question, level of analysis, modeling paradigms, computational properties, and iterative model development. In addition to deriving our framework from a concise literature analysis, we discuss challenges and potentials of cognitive modeling. We expect cognitive models to leverage personalized human behavior prediction, agent behavior generation, and interaction pretraining as well as adaptation, which we outline with application examples from personalized HRI

Bayesian Theory of Mind for False Belief Understanding in Human-Robot Interaction

In order to achieve a widespread adoption of social robots in the near future, we need to design intelligent systems that are able to autonomously understand our beliefs and preferences. This will pave the foundation for a new generation of robots able to navigate the complexities of human societies. To reach this goal, we look into Theory of Mind (ToM): the cognitive ability to understand other agents’ mental states. In this paper, we rely on a probabilistic ToM model to detect when a human has false beliefs with the purpose of driving the decision-making process of a collaborative robot. In particular, we recreate an established psychology experiment involving the search for a toy that can be secretly displaced by a malicious individual. The results that we have obtained in simulated experiments show that the agent is able to predict human mental states and detect when false beliefs have arisen. We then explored the set-up in a real-world human interaction to assess the feasibility of such an experiment with a humanoid social robot

Using LEGO® SERIOUS® Play with stakeholders for RRI

This paper discusses Responsible (Research and) Innovation (RRI) within a UKRI project funded through the Trustworthy Autonomous Systems Hub, Imagining Robotic Care: Identifying conflict and confluence in stakeholder imaginaries of autonomous care systems. We used LEGO® Serious Play® as an RRI methodology for focus group workshops exploring sociotechnical imaginaries about how robots should (or should not) be incorporated into the existing UK health-social care system held by care system stakeholders, users and general publics. We outline the workshops’ protocol and some emerging insights from early data collection, including the ways that LSP aids in the surfacing of tacit knowledge, allowing participants to develop their own scenarios and definitions of ‘robot’ and ‘care’. We further discuss the implications of LSP as a method for upstream stakeholder engagement in general and how this may contribute to embedding RRI in robotics research on a larger scale

Using a Personalised Socially Assistive Robot for Cardiac Rehabilitation: A Long-Term Case Study

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Emotion and memory model for social robots: a reinforcement learning based behaviour selection

In this paper, we propose a reinforcement learning (RL) mechanism for social robots to select an action based on users’ learning performance and social engagement. We applied this behavior selection mechanism to extend the emotion and memory model, which allows a robot to create a memory account of the user’s emotional events and adapt its behavior based on the developed memory. We evaluated the model in a vocabulary-learning task at a school during a children’s game involving robot interaction to see if the model results in maintaining engagement and improving vocabulary learning across the four different interaction sessions. Generally, we observed positive findings based on child vocabulary learning and sustaining social engagement during all sessions. Compared to the trends of a previous study, we observed a higher level of social engagement across sessions in terms of the duration of the user gaze toward the robot. For vocabulary retention, we saw similar trends in general but also showing high vocabulary retention across some sessions. The findings indicate the benefits of applying RL techniques that have a reward system based on multi-modal user signals or cues

FABRIC: A Framework for the Design and Evaluation of Collaborative Robots with Extended Human Adaptation

A limitation for collaborative robots (cobots) is their lack of ability to adapt to human partners, who typically exhibit an immense diversity of behaviors. We present an autonomous framework as a cobot's real-time decision-making mechanism to anticipate a variety of human characteristics and behaviors, including human errors, toward a personalized collaboration. Our framework handles such behaviors in two levels: 1) short-term human behaviors are adapted through our novel Anticipatory Partially Observable Markov Decision Process (A-POMDP) models, covering a human's changing intent (motivation), availability, and capability; 2) long-term changing human characteristics are adapted by our novel Adaptive Bayesian Policy Selection (ABPS) mechanism that selects a short-term decision model, e.g., an A-POMDP, according to an estimate of a human's workplace characteristics, such as her expertise and collaboration preferences. To design and evaluate our framework over a diversity of human behaviors, we propose a pipeline where we first train and rigorously test the framework in simulation over novel human models. Then, we deploy and evaluate it on our novel physical experiment setup that induces cognitive load on humans to observe their dynamic behaviors, including their mistakes, and their changing characteristics such as their expertise. We conduct user studies and show that our framework effectively collaborates non-stop for hours and adapts to various changing human behaviors and characteristics in real-time. That increases the efficiency and naturalness of the collaboration with a higher perceived collaboration, positive teammate traits, and human trust. We believe that such an extended human adaptation is key to the long-term use of cobots.Comment: The article is in review for publication in International Journal of Robotics Researc