POLITECNICO DI TORINO Master of science in Mechatronic engineering Master Degree Thesis Learning from humans to improve Socially-Aware motion planning

In the near future, one of the most likely scenarios is the daily coexistence of humans and robots. This scenario will be witnessed, for instance, in assistive robots for seniors, cleaning robots, reception robots in public or private shops. Although there already exist technological tools to perform a secure motion in a crowded environment, not too many authors have studied if a robot trajectory would be pleasant and accepted by humans. The thesis fits in this scenario and is focused on the trajectory generation for a mobile robot in order to be acceptable and not annoying humans. How can we reach this ambitious goal? Humans are more successful in planning a collision-free trajectory with mutual avoidance manoeuvres in a populated environment than any motion planning algorithm developed so far. While humans can easily deal with predicting the motion of surrounding people, robotic systems are still facing problems. Moreover, humans tend to attribute intentions and consciousness to non-human entity. In literature, this behaviour is named anthropomorphizing. Here, we leverage anthropomorphizing to improve human-robot interaction and, thus, raise the acceptance for humans. This is achieved by attempting to design the robot motion in a human-like manner. For these reasons, we want to improve the robot navigation starting from the study of humans’ decisions. The majority of the approaches concentrate their attention to predict human motion individually without considering the interaction between humans. Here, we model the motion of humans considering the interaction between and with pedestrians using a game-theoretic approach. Generally, many motion planning approaches at the state of the art have a reactive behaviour, i.e. they avoid obstacles without considering the prediction of human motion and the interaction with them. Game theory has a lot of advantages over reactive methods, in fact, it can model the mutual anticipation of the influence of other agents and adapt their own decisions based on the possible actions of others. In this thesis, non-cooperative game theory is applied to predict the decision of multiple humans that interact with each other during navigation. Hence, the concept of Nash equilibrium in dynamic games is applied to solve our model. In the last decades, some scientists studied human motion from a game-theoretic point of view, but their models comprised two people. Moreover, static obstacles and groups of people were not accounted for. Here, we have reformulated the problem considering a different cost function, extending the model considering multiple people and detecting groups of them, as well as evaluating patterns of natural interaction between humans and static objects. The model has been validated with real-world surveillance videos, qualitatively comparing real trajectories and the output of our model. In the second part of the dissertation, we used the model previously developed, to create a human-like trajectory for an autonomous robot that navigates among multiple humans, considering the robot as a player of the game. In the end, with a variation of the Turing test, we tried to evaluate quantitatively whether the final motion robot planning is socially acceptable for humans. This quality has been measured considering the human likeness of the trajectory generated by our motion planner. 50 volunteers participated in our test and the collected data validates the proposed approach.