We expand on previous work for negotiating human-robot navigation contention around doorways to produce a more socially-compliant autonomous robot behaviour. Our goal is to improve the integration of robots navigating in human environments by eliciting human recognition of the robot's right of way. This is achieved by incorporating feedback from a user study of our previous system to create a more communicative, reciprocal, and assertive behaviour. Our contribution includes both the updated behaviour and a new user study that evaluates and compares the system to its predecessor. Results show that participants are more likely to respect the robot's right of way given the new robot behaviour, but their responses also highlight the challenges of socially integrating robots into human spaces.

We propose and test an autonomous robot behavior for socially-compliant navigation of doorways with both human and robot interlocutors. Building on previous work for “aggressive” interaction between robots to resolve navigation deadlocks in corridors, we demonstrate an “assertive” robot that negotiates right-of-way when faced with a human or other robot. The negotiation is implemented using only motion and common navigation sensors, without explicit message-passing. Our goal is for the correct agent to take priority, as decided both by time-efficiency and as judged subjectively by naive human participants. Our contribution is a practical method for doorway negotiation, and a study of human users' responses to a robot that appears to participate in existing social customs surrounding doors. Our method is evaluated with robot-robot experiments and a human-robot interaction study with nonexpert users.

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.