Three advanced natural interaction modalities for mobile robot guidance in an indoor environment were developed and compared using two tasks and quantitative metrics to measure performance and workload. The first interaction modality is based on direct physical interaction requiring the human user to push the robot in order to displace it. The second and third interaction modalities exploit a 3-D vision-based human-skeleton tracking allowing the user to guide the robot by either walking in front of it or by pointing toward a desired location. In the first task, the participants were asked to guide the robot between different rooms in a simulated physical apartment requiring rough movement of the robot through designated areas. The second task evaluated robot guidance in the same environment through a set of waypoints, which required accurate movements. The three interaction modalities were implemented on a generic differential drive mobile platform equipped with a pan-tilt system and a Kinect camera. Task completion time and accuracy were used as metrics to assess the users' performance, while the NASA-TLX questionnaire was used to evaluate the users' workload. A study with 24 participants indicated that choice of interaction modality had significant effect on completion time (F (2, 61) = 84.874, p <; 0.001), accuracy (F (2, 29) = 4.937, p = 0.016), and workload (F (2, 68) = 11.948, p <; 0.001). The direct physical interaction required less time, provided more accuracy and less workload than the two contactless interaction modalities. Between the two contactless interaction modalities, the person-following interaction modality was systematically better than the pointing-control one: The participants completed the tasks faster with less workload.

The video shows the interaction with a customized Kompa\"{i} robot. The robot consists of the Robosoft's robuLAB10 platform, tablet PC, and a Microsoft Kinect camera mounted on a pan-tilt system. A visual control algorithm provides continuous person tracking. The newly developed robot features include gesture recognition, person following, navigation with pointing, and force control, which were integrated with the Robosoft's robuBOX SDK and the Karto SLAM algorithms. The video demonstrates all the features and puts the robot in use in an everyday home scenario.

There is limited knowledge on how to design effective interfaces to enable nonexpert users to interact with robot learning algorithms. This paper focuses on an interface design challenge: How to provide the user with sufficient information about the learned behavior. A simulated robotic task where the robot has online learning capabilities was developed. This platform was used to study the impact of the variability of the environmental conditions, the information provided on the relation between the learned robot behavior and the conditions in the environment, and the presence of a preview of the learned robot behavior on the use of the system and on task performance. The results show significant effects of the type and the quantity of displayed information. Forty-two participants made the best use of brief and contextualized notifications about changes in the environment: their presence improved the overall performance and the usage of the automation and reduced the workload. In contrast, adding previews of the learned behavior surprisingly impaired performance and reduced the use of automation.

We studied the effects of changes of system sensitivity over time and of providing notification about these changes on the responses to binary warnings in a simulated robotic task. Participants had to decide how much risk to take based on the output of a binary warning indicator. Experimental conditions differed in the presence or absence of notifications about changes of sensitivity of the warning system. Such changes happened twice during the experiment. The first change was an increase and the second a decrease of the sensitivity, returning it to its initial value. Participants successfully adapted their responses to the changes when notifications about the changes were displayed. However, without notifications, they did not properly adjust their response to changes, and their levels of compliance and reliance were differentially affected by the change, indicating a distinction between these two dimensions of trust.

In this paper, we present the adaptation of a sensorless (in De Luca's sense [1], i.e., without the use of extra sensors,) collision detection approach previously used on robotic arms to mobile wheeled robots. The method is based on detecting the torque disturbance and does not require a model of the robot's dynamics. We then consider the feasibility of developing control by physical interaction strategies using the described adapted technique.