Abstract
Robot motion planners are increasingly being equipped with an intriguing property: human likeness. This property can enhance human–robot interactions and is essential for a convincing computer animation of humans. This paper presents a (multi-agent) motion planner for dynamic environments that generates human-like motion. The presented motion planner stands out against other motion planners by explicitly modeling human-like decision making and taking interdependencies between individuals into account, which is achieved by applying game theory. Non-cooperative games and the concept of a Nash equilibrium are used to formulate the decision process that describes human motion behavior while walking in a populated environment. We evaluate whether our approach generates human-like motions through two experiments: a video study showing simulated, moving pedestrians, wherein the participants are passive observers, and a collision avoidance study, wherein the participants interact within virtual reality with an agent that is controlled by different motion planners. The experiments are designed as variations of the Turing test, which determines whether participants can differentiate between human motions and artificially generated motions. The results of both studies coincide and show that the participants could not distinguish between human motion behavior and our artificial behavior based on game theory. In contrast, the participants could distinguish human motions from motions based on established planners, such as the reciprocal velocity obstacles or social forces.
Highlights
IntroductionCars, and robots are merely inanimate objects. people assign human attributes to these objects
Computers, cars, and robots are merely inanimate objects
Driven by the motivation discussed at the beginning, we summarize our goal as follows: Goal: a motion planner that generates human-like motion behavior for robots acting in populated environments
Summary
Cars, and robots are merely inanimate objects. people assign human attributes to these objects. Objects can be perceived as having beliefs, consciousness and intentions [21,47,53,54,86] We can exploit anthropomorphizing to enhance human–robot interaction in general [5,35,40] and robot motion planning in particular [10, 11,13]. This is achieved by adding an additional attribute to the robot: human likeness
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