Band 7, Heft 1

This article reports observations from a field study in which medical responders used a social telepresence robot to communicate with participants playing the role of a trapped victim in two search and rescue exercises. The interaction between the robot, victims, and responders suggests the coexistence of two distinct social identities for the robot. One which is a pure conduit for the remote medic, and another in which the robot is treated as an independent social actor. Participants acting as victims demonstrated fluidity in interacting with each identity. The social identify of a robot has important implications for the development of future telepresence systems, particularly in the healthcare domain. Since victims in the exercises gave attention to both the robot and the remote medic, it is possible that the robot’s social actor role may divert attention from the remotely connected individual. The work provides a starting point for investigation of role conflict between a remote medical professional and the robot they are using to assist a patient.

The teleoperation of robot manipulators over the internet suffers from variable delays in the communications. Here we address a tele-assistance scenario, where a remote operator assists a disabled or elderly user on daily life tasks. Our behavioral approach uses local environment information from robot sensing to help enable faster execution for a given movement tolerance. This is achieved through a controller that automatically slows the operator down before having collisions, using a set of distributed proximity sensors. The controller is made to gradually increase the assistance in situations similar to those where ollisions have occurred in the past, thus adapting to the given operator, robot and task-set. Two controlled virtual experiments for tele-assistance with a 5 DOF manipulator were performed, with 300 ms and 600 ms mean variable round-trip delays. The results showed significant improvements in the median times of 12.6% and 16.5%, respectively. Improvements in the subjective workload were also seen with the controller. A first implementation on a physical robot manipulator is described.

This article describes an implementation of a hardware-based modeling system for multi-robot collaborative tasks. Authors propose to perform a hardware-based modeling phase as an intermediate step between software simulation and implementation within a real application. In this article hardware and software architectures of the offered system as well as an application environment for collaborative multi-robot system studies are considered.

Learning skills or knowledge online from experiences is attractive for robots because it permits them to develop new behavior autonomously. However, the onus lies with the system designer to specify which skills or knowledge the robot should learn. Experience-based goal generation algorithms permit a robot to decide autonomously what it will to learn. This paper presents an adaptive resonance theory approach to experience-based generation of approach, avoidance, maintenance and achievement goals for a mobile robot. An experimental analysis is conducted to explore the relationship between algorithm parameters and goals generated on a simulated ePuck robot. Results show how parameter choice influences the number, stability and nature of generated goals. We identify theweight representations, distance functions and update rules that are appropriate for a mobile robot to generate maintenance and achievement goals.

Humans can express their own emotion and estimate the emotional states of others during communication. This paper proposes a unified model that can estimate the emotional states of others and generate emotional self-expressions. The proposed model utilizes a multimodal restricted Boltzmann machine (RBM) —a type of stochastic neural network. RBMs can abstract latent information from input signals and reconstruct the signals from it. We use these two characteristics to rectify issues affecting previously proposed emotion models: constructing an emotional representation for estimation and generation for emotion instead of heuristic features, and actualizing mental simulation to infer the emotion of others from their ambiguous signals. Our experimental results showed that the proposed model can extract features representing the distribution of categories of emotion via self-organized learning. Imitation experiments demonstrated that using our model, a robot can generate expressions better than with a direct mapping mechanism when the expressions of others contain emotional inconsistencies.Moreover, our model can improve the estimated belief in the emotional states of others through the generation of imaginary sensory signals from defective multimodal signals (i.e., mental simulation). These results suggest that these abilities of the proposed model can facilitate emotional human–robot communication in more complex situations.

An increasing number of companion robots have started reaching the public in the recent years. These robots vary in their appearance and behavior. Since these two factors can have an impact on lasting human–robot relationships, it is important to understand their effect for companion robots.We have conducted an experiment that evaluated the impact of a robot’s appearance and its behaviour in repeated interactions on its perceived empathy, trustworthiness and anxiety experienced by a human. The results indicate that a highly humanlike robot is perceived as less trustworthy and empathic than a more machinelike robot. Moreover, negative behaviour of a machinelike robot reduces its trustworthiness and perceived empathy stronger than for highly humanlike robot. In addition, we found that a robot which disapproves of what a human says can induce anxiety felt towards its communication capabilities. Our findings suggest that more machinelike robots can be more suitable as companions than highly humanlike robots. Moreover, a robot disagreeing with a human interaction partner should be able to provide feedback on its understanding of the partner’s message in order to reduce her anxiety.