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These days, physical human-robot interaction (pHRI) of social robots has received a lot of attention. This paper proposes a method for variable admittance control (VAC) based pHRI to enable social robots to perform various social gestures. The proposed method includes several schemes. Firstly, a scheme to adjust the robot’s damping is proposed, which takes into account the external torques exerted by the user and the robot’s pose to control movement speed, reflecting the workspaces of both the human and the robot. Secondly, a scheme to change the joint stiffness is proposed, considering the reference angle and the external forces from the user to generate active motions for cooperative pHRI. Additionally, a scheme to change the robot reference point is proposed, which adjusts based on the user’s external forces and the robot’s posture for pHRI in various situations. The proposed methods are implemented with a dual-arm social robot. To realize pHRI based on the proposed schemes, a generalized momentum-based disturbance observer, one of the sensorless disturbance observers, is employed to estimate the exerted torque. To verify the performance of the proposed methods, experiments are carried out for handshaking and hugging.

Because the internal structure, degree of freedom, skin control position and range of the android face are different, it is very difficult to generate facial expressions by applying existing facial expression generation methods. In addition, facial expressions differ among robots because they are designed subjectively. To address these problems, we developed a system that can automatically generate robot facial expressions by combining an android, a recognizer capable of classifying facial expressions and a genetic algorithm. We have developed two types (older men and young women) of android face robots that can simulate human skin movements. We selected 16 control positions to generate the facial expressions of these robots. The expressions were generated by combining the displacements of 16 motors. A chromosome comprising 16 genes (motor displacements) was generated by applying real-coded genetic algorithms; subsequently, it was used to generate robot facial expressions. To determine the fitness of the generated facial expressions, expression intensity was evaluated through a facial expression recognizer. The proposed system was used to generate six facial expressions (angry, disgust, fear, happy, sad, surprised); the results confirmed that they were more appropriate than manually generated facial expressions.