Abstract - Fuzzy Systems are an efficient instrument to create efficient and transparent models of the behavior of complex dynamic systems such as autonomous humanoid robots. The human interpretability of these models is particularly significant when it is applied to the cognitive robotics research, in which the models are designed to study the behaviors and produce a better understanding of the underlying processes of the cognitive development. From this research point of view, this paper presents a comparative study on training fuzzy based system to control the autonomous navigation and task execution of a humanoid robot controlled in a soccer scenario. Examples of sensor data are collected via a computer simulation, then we compare the performance of several fuzzy algorithms able to learn and optimize the humanoid robot's actions from the data.
Abstract - Human friendly service robots should possess human like interaction and reasoning capabilities. Humans prefer to use voice instructions in order to communicate with peers. Those voice instructions often include linguistic notions and descriptors that are fuzzy in nature. Therefore, the human friendly robots should be capable of understanding the fuzzy information in user instructions. This paper proposes a method in order to interpret directional information in navigational user commands by considering the environment dependent fuzziness associated with the directional linguistic notions. A module called Direction Interpreter has been introduced for handling the fuzzy nature of directional linguistic notions. The module has been implemented with a fuzzy logic system that is capable of modifying the perception of the robot about the directional information according to the surrounding environment of the robot. This modification is done by weighting the output membership function with the distribution of the free space around the robot. According to the experimental results, the proposed system is capable of replicating the natural directional perception of humans that depends on the environment to a greater extent than the existing approaches.
Abstract - Human-robot interaction mechanisms are being developed to cater to growing elderly and disabled population. There are still voids in achieving human-likeness before initiation of an interaction. Interaction scenario could be made interesting and effective by engraving basic cognitive skills into the robot's intelligence. Skills related to human-like interaction depends on cognitive skills and interpretation of the existing situation. Most robot users encounter a common problem with their robots. That is robot trying to interact with the user when he's engaged. In robot's perspective, the robot is not fully capable of deciding when to interact with the user. This paper presents a model to decide when to interact with the user, minimizing such failures. The proposed model has separate functional units for decision making on a user's nonverbal interaction demanding. User's availability for interaction is deduced through extracted information. The system observes a user for his bodily movements and behavior for a specified time duration. The extracted information is analyzed and then put through a module called Interaction Demanding Pose Identifier to interpret the interaction demanding of the user. The identified pose and other calculated parameters are fed into the Fuzzy Interaction Decision Making Module in order to interpret the degree of interaction demanding of the user. Interaction demanding is taken into consideration before going for direct interaction with the user. This method is implemented and tested in a simulated domestic environment with users in a broad age gap. Implementation of the method and results of the experiment are presented.
Abstract - The study of the interaction between autonomous robots and human agents in common working areas is an emerging field of research. Main points thereby are human safety, system stability, performance and optimality of the whole interaction process. Two approaches to deal with human-robot interaction can be distinguished: Long distance prediction which requires the recognition of intentions of other agents, and short distance control which deals with actions and reactions between agents and mutual reactive control of their motions and behaviors. In this context obstacle avoidance plays a prominent role. In this paper long distance prediction is represented by the identification of human intentions to use specific lanes by using fuzzy time clustering of pedestrian tracks. Another issue is the extrapolation of parts of both human and robot trajectories in the presence of scattered/uncertain measurements to guarantee a collision-free robot motion. Short distance control is represented by obstacle avoidance between agents using the method of velocity obstacles and both analytical and fuzzy control methods.
Abstract - A robotic system that is designed to coexist with humans has to adapt its behavioral and social interaction parameters not only with respect to the task it is supposed to accomplish, but also with respect to the human being it is interacting with by profiling her habits, preferences, and personality. This is particularly relevant in the domain of assistive robotics where the behavioral adaptability has been shown to enhance the users' acceptability of a robot. In this work, we propose a neuro-fuzzy-bayesian system able to adapt the robot proxemics behavior with respect to the human users' personality and the action she is currently performing. The user's personality is evaluated according to the Big-Five factors model and the activity recognition is obtained by classifying data from a wearable device through the use of a Bayesian Network classifier. As shown by a statistical study, the proposed framework is capable of computing the most appropriate robot proxemics behavior in order to improve human feeling in interacting with artificial agents, such as robots.