Cognitive Architecture to Generate Motivational Feelings: A Way to Improve Visual Learning in Robots

Expressions and voice pitch of an individual play an indispensable role in different cognitive processes. These factor help humans to learn a lot about different things present in their environment. This paper proposes a way to motivate robot learning through their environment and human around them. This mechanism is based on recognition of other agent’s facial expressions and voice pitch analysis by robot. A motivational level can be calculated through these feelings. Motivational level can impel the robots to improve their past learning. This mechanism can possibly help a robot to apprehend its environment and interact with other agents effectively. Keywords: cognition; motivation; facial expression; voice pitch; perception; memory

Qualification of a Collaborative Human-robot Welding Cell

AbstractThis work is focused on evaluating performance of a collaborative robot welding cell developed in our previous research. Such a cell is based on an interactive cooperation between a human supervisor and a welding robot. This approach to organizing a workstation allows to employ robots even in the case of prototypes or small productions. Research on collaborative robots usually focuses on safety issues and on the programming techniques. Present work deals with a complementary problem crucial to industrial applications: the qualification of the welding cell performance in terms of accuracy, repeatability and dependability.In this application, the human worker is responsible for handling of the parts to be assembled and for teaching the robot. The robot is in charge of actual welding. Teaching is executed by demonstration: the teacher shows the welding trajectories with a pointer observed by a motion capture system. The program is generated automatically and executed by the robot. Robots and humans share the same workspace in different times therefore human risk exposure is minimal.Industrial applications of this or similar technology require that the process reliability and capability be assessed. We describe and analyze error accumulation along the entire data flow from the measurement tool, through the reference system transformations, to the actual representation and execution of the robot program

Improving the Expressiveness of Mobile Robots

RoboX is a tour guide robot, designed to interact with visitors at Expo02, the Swiss National Exhibition. During five months, ten RoboXs will be in contact with people not trained to autonomous robots and will guide them throughout an exhibition about robotics. The work exposed here was dedicated to make RoboX lifelike and credible in implementing an expression generator. This generator uses the robot capacities in order to best express its internal state which is influenced by its sensor entries and the scenario of the guided tour. In order to validate our approach, a short experiment was set up in order to present results that include appreciations of people not used to robots

Social behaviours in dog-owner interactions can serve as a model of companion robot behaviour

It is essential for social robots to fit in the human society. In order to facilitate this process we propose to use the family dog’s social behaviour shown towards humans as an inspiration. In this study we explored dogs’ low level social monitoring in dog-human interactions and extracted individually consistent and context dependent behaviours in simple everyday social scenarios. We found that proximity seeking and tail wagging were most individually distinctive in dogs, while activity, orientation towards the owner, and exploration were dependent on the context and/or the activity of the owner. The functional analogues of these dog behaviours can be implemented in social robots of different embodiments in order to make them acceptable and more believable for humans

Anticipatory Robot Control for a Partially Observable Environment Using Episodic Memories

This paper explains an episodic-memory based approach for computing anticipatory robot behavior in a partially observable environment. Inspired by biological findings on the mammalian hippocampus, here, the episodic memories retain a sequence of experienced observation, behavior, and reward. Incorporating multiple machine learning methods, this approach attempts to help reducing the computational burden of the partially observable Markov decision process (POMDP). In particular, the proposed computational reduction techniques include: 1) abstraction of the state space via temporal difference learning; 2) abstraction of the action space by utilizing motor schemata; 3) narrowing down the state space in terms of the goals by employing instance-based learning; 4) elimination of the value-iteration by assuming a unidirectional-linear-chaining formation of the state space; 5) reduction of the state-estimate computation by exploiting the property of the Poisson distribution; and 6) trimming the history length by imposing the cap on the number of episodes that are computed. Furthermore, claims 5) and 6) were empirically verified, and it was confirmed that the state estimation can be in fact computed in an O(n) time (where n is the number of the states), more efficient than a conventional Kalman-filter based approach of O(n2)

Cognitive and affective motivation in conceptual modelling

A proposal is presented towards the extension of conceptual models of information systems, in order to allow specification and simulation of the behaviour of agents with an adequate degree of realism. Our method is mainly based on rules to infer the goals of agents from situations holding at given states. In this paper, we argue that the rules should take into account both cognitive and affective characteristics, as can be conveyed, for the various agents, by their individual profiles and current internal states. Such characteristics should also influence the choice of strategies to handle goal interferences in multi-goal/multi-agent environments.Keywords: Conceptual Modelling, Simulation, Multi-Agents, Affective Motivation, Goal Interference

Designing Social Robots for Early Detection of Mental Heath Conditions

Globally, mental health is a growing socio-economic burden and leads to negative ramifications including mortality and poor quality of life. Successful early detection of mental illness will make a significant, positive economic and societal impact. Social robots show potential to be integrated as tools for psychological therapy and early detection. This thesis seeks to design and develop social robots for early detection of mental illness. I explore how multi-modal inputs can be used to infer user's mental state and to direct appropriate robot behavior. I have employed an iterative design process for the design of robot morphology, personality, and behavior. Design 1 is a social robot with 6 DOF and exhibits non-verbal behaviors. In this design, I explore audio, video, and haptic inputs to detect user's emotional state. Design 2 is an interactive device that aims to collect audio data for the detection of early signs of depression. In this design, acoustic features are explored for depression detection, and the device uses audio and LEDs to communicate it's internal state. Finally, I have conducted a pilot experiment to investigate how the users interact with the robot. This thesis informs the design of future robots that aim to support early detection of mental illnesses