Low throughput user interface Bayesian programming Brain-computer interface Neurorobotics EEG Error-related potentials a b s t r a c t This paper presents a novel semi-autonomous navigation strategy designed for low throughput interfaces. A mobile robot (e.g. intelligent wheelchair) proposes the most probable action, as analyzed from the environment, to a human user who can either accept or reject the proposition. In the case of refusal, the robot will propose another action, until both entities agree on what needs to be done.In an unknown environment, the robotic system first extracts features so as to recognize places of interest where a human-robot interaction should take place (e.g. crossings). Based on the local topology, relevant actions are then proposed, the user providing answers by means of a button or a brain-computer interface (BCI). Our navigation strategy is successfully tested both in simulation and with a real robot, and a feasibility study for the use of a BCI confirms the potential of such an interface.

Abstract-The exploitation of EEG signatures of cognitive processes can provide valuable information to improve interaction with brain actuated devices. In this work we study these correlates in a realistic situation simulated in a virtual reality environment. We focus on cortical potentials linked to the anticipation of future events (i.e. the contingent negative variation, CNV) and error-related potentials elicited by both visual and tactile feedback. Experiments with 6 subjects show brain activity consistent with previous studies using simpler stimuli, both at the level of ERPs and single trial classification. Moreover, we observe comparable signals irrespective of whether the subject was required to perform motor actions. Altogether, these results support the possibility of using these signals for practical brain machine interaction.

Abstract-This paper presents a semi-autonomous navigation strategy aimed at the control of assistive devices (e.g. an intelligent wheelchair) using low throughput interfaces. A mobile robot proposes the most probable action, as analyzed from the environment, to a human user who can either accept or reject the proposition. In case of rejection, the robot will propose another action, until both entities agree on what needs to be done. In a known environment, the system infers the intended goal destination based on the first executed actions. Furthermore, we endowed the system with learning capabilities, so as to learn the user habits depending on contextual information (e.g. time of the day or if a phone rings). This additional knowledge allows the robot to anticipate the user intention and propose appropriate actions, or goal destinations.

This paper presents a comparison between six different ways to convey navigational information provided by a robot to a human. Visual, auditory, and tactile feedback modalities were selected and designed to suggest a direction of travel to a human user, who can then decide if he agrees or not with the robot's proposition. This work builds upon a previous research on a novel semi-autonomous navigation system in which the human supervises an autonomous system, providing corrective monitoring signals whenever necessary.We recorded both qualitative (user impressions based on selected criteria and ranking of their feelings) and quantitative (response time and accuracy) information regarding different types of feedback. In addition, a preliminary analysis of the influence of the different types of feedback on brain activity is also shown. The result of this study may provide guidelines for the design of such a human-robot interaction system, depending on both the task and the human user.

User guidance systems are relevant to various applications of the service robotics field, among which: smart GPS navigator, robotic guides for museum or shopping malls or robotic wheel chairs for disabled persons. Such a system aims at helping its user to reach its destination in a fairly complex environment. If we assume the system is used in a fixed environment by multiple users for multiple navigation task over the course of days or weeks, then it is possible to take advantage of the user routine: from the initial navigational choice, users can be identified and their goal can be predicted. As a result of these prediction, the guidance system can bring its user to its destination while requiring less interaction. This property is particularly relevant for assisting disabled person for whom interaction is a long and complex task. In this paper, we implement a user guidance system using a dynamic Bayesian model and a topological representation of the environment. This model is evaluated with respect to the quality of its action prediction in a scenario involving 4 human users, and it is shown that in addition to the user identity, the goals and actions of the user are accurately predicted.