Biologically Inspired Controller of Human Action Behaviour for a Humanoid Robot in a Dyadic Scenario

Abstract

Humans have a particular way of moving their body when interacting with the environment and with other humans. The movement of the body is commonly known and expresses the intention of the action. The express of intent by our movement is classified as non-verbal cues, and from them, it is possible to understand and anticipate the actions of humans. In robotics, humans need to understand the intention of the robot in order to efficiently and safely interact in a dyadic activity. If robots could possess the same non-verbal cues when executing the same actions, then humans would be capable of interacting with robots the way they interact with other humans.We propose a robotic controller capable of executing actions of moving objects on a table (placing) and handover objects to humans (giving) in a human-like behaviour. Our first contribution is to model the behaviour of the non-verbal cues of a human interacting with other humans while performing placing and giving actions. From the recordings of the motion of the human, we build a computational model of the trajectory of the head, torso, and arm for the different actions. Additionally, the human motion model was consolidated with the integration of a previously developed human gaze behaviour model. As a second contribution, we embedded this model in the controller of an iCub humanoid robot and compared the generated trajectories to the real human model, and additionally, compare with the existing minimum-jerk controller for the iCub (iKin).Our results show that it is possible to model the complete upper body human behaviour during placing and giving interactions, and the generated trajectories from the model give a better approximation of the human-like behaviour in a humanoid robot than the existing inverse kinematics solver. From this work, we can conclude that our controller is capable of achieving a humanlike behaviour for the robot which is a step towards robots capable of understanding and being understood by humans.