We present a simple probabilistic framework for multimodal sensor fusion that allows a mobile robot to reliably locate and approach the most promising interaction partner among a group of people, in an uncontrolled environment. Our demonstration integrates three complementary sensor modalities, each of which detects features of nearby people. The output is an occupancy grid approximation of a probability density function over the locations of people that are actively seeking interaction with the robot. We show empirically that simply driving towards the peak of this distribution is sufficient to allow the robot to correctly engage an interested user in a crowd of bystanders.

We report the actions of untrained users when instructed to “ make the robot come to you”. The robot is a generic wheeled mobile robot located 8m away and is driven by the experimenter without the knowledge of the participant. The results show a variety of calls and gestures made to the robot, that changed over time. We observed two distinct behaviour phases: (i) getting the robot's attention; (ii) maintaining the robot's behaviour as it approaches. This data could aid the design of future mobile-robot HRI.

We present an integrated human-robot interaction system that enables a user to select and command a team of two Unmanned Aerial Vehicles (UAV) using voice, touch, face engagement and hand gestures. This system integrates multiple human [multi]-robot interaction interfaces as well as a navigation and mapping algorithm in a coherent semi-realistic scenario. The task of the UAVs is to explore and map a simulated Mars environment.

We present a multi-modal multi-robot interaction whereby a user can identify an individual or a group of robots using haptic stimuli, and name them using a voice command (e.g."You two are green"). Subsequent commands can be addressed to the same robot(s) by name (e.g. "Green! Take off!"). We demonstrate this as part of a real-world integrated system in which a user commands teams of autonomous robots in a coordinated exploration task.

We describe a system whereby multiple humans and mobile robots interact robustly using a combination of sensing and signalling modalities. Extending our previous work on selecting an individual robot from a population by face-engagement, we show that reaching toward a robot - a specialization of pointing - can be used to designate a particular robot for subsequent one-on-one interaction. To achieve robust operation despite frequent sensing problems, the robots use three phases of human detection and tracking, and emit audio cues to solicit interaction and guide the behaviour of the human. A series of real-world trials demonstrates the practicality of our approach.

We present a multimodal system for creating, modifying and commanding groups of robots from a population. Extending our previous work on selecting an individual robot from a population by face engagement, we show that we can dynamically create groups of a desired number of robots by speaking the number we desire, e.g. “You three”, and looking at the robots we intend to form the group. We evaluate two different methods of detecting which robots are intended by the user, and show that an iterated election performs well in our setting. We also show that teams can be modified by adding and removing individual robots: “And you. Not you”. The success of the system is examined for different spatial configurations of robots with respect to each other and the user to find the proper workspace of selection methods.