Collision avoidance during locomotion can be achieved by a variety of strategies. While in some situations only a single trajectory will successfully avoid impact, in many cases several different strategies are possible. Locomotor experiments in the presence of static boundary conditions have suggested that the choice of an appropriate trajectory is based on a maximum-smoothness strategy. Here we analyzed locomotor trajectories of subjects avoiding collision with another human crossing their path orthogonally. In such a case, changing walking direction while keeping speed or keeping walking direction while changing speed would be two extremes of solving the problem. Our participants clearly favored changing their walking speed while keeping the path on a straight line between start and goal. To interpret this result, we calculated the costs of the chosen trajectories in terms of a smoothness-maximization criterion and simulated the trajectories with a computational model. Data analysis together with model simulation showed that the experimentally chosen trajectory to avoid collision with a moving human is not the optimally smooth solution. However, even though the trajectory is not globally smooth, it was still locally smooth. Modeling further confirmed that, in presence of the moving human, there is always a trajectory that would be smoother but would deviate from the straight line. We therefore conclude that the maximum smoothness strategy previously suggested for static environments no longer holds for locomotor path planning and execution in dynamically changing environments such as the one tested here.

No abstract

Our objective is to improve legibility of robot navigation behavior in the presence of moving humans. We examine a human-aware global navigation planner in a path crossing situation and assess the legibility of the resulting navigation behavior. We observe planning based on fixed social costs and static search spaces to perform badly in situations where robot and human move towards the same point. To find an improved cost model, we experimentally examine how humans deal with path crossing. Based on the results we provide a new way of calculating social costs with context dependent costs without increasing the search space. Our evaluation shows that a simulated robot using our new cost model moves more similar to humans. This shows how comparison of human and robot behavior can help with assessing and improving legibility.

No abstract

Humans interact safely, effortlessly, and intuitively with each other. An efficient robot assistant should thus be able to interact in the same way. This requires not only that the robot can react appropriately to human behaviour, but also that robotic behaviour can be understood intuitively by the human partners. The latter can be achieved by the robot mimicking certain aspects of human behaviour so that the human partner can more easily infer the intentions of the robot. Here we investigate a simple interaction scenario, approach and hand-over, to gain better understanding of the behavioural patterns in human-human interactions. In our experiment, one human subject, holding an object, approached another subject with the goal to hand over the object. Head and object positions were measured with a motion tracking device to analyse the behaviour of the approaching human. Interaction indicated by lifting the object in order to prepare for hand-over started approximately 1.2 s before the actual hand-over. Interpersonal distance varied considerably between subjects with an average of 1.16 m. To test whether the behavioural patterns observed depended on two humans being present, we replaced in a second experiment the receiving subject with a table. We found that the behaviour of the transferring subject was very similar in both scenarios. Thus, the presence of the receiving subject plays a minor role in determining parameters such as start of interaction or interaction distance. We aim to implement and test the parameters derived experimentally in a robotic assistant to improve and facilitate human-robot interaction.