Evaluation of Robotic Mood Transition Due to Individual Personality

It is desirable that a robot behaves differently in different interaction scenarios. For example, to keep attention from students in education applications, the robot needs to behave more friendly and funny. Hence the openness and agreeableness scales are designed higher.

One can design the desired personality by adjusting the corresponding Big Five factors. In this experiment, two opposite robotic individual personalities were designed respectively for RobotA (with more active trait) and RobotB (with more passive trait). The Big Five factors were applied to model these two personalities. Table 4-3 lists the assigned scales corresponding to both opposite personalities. As we know, people belonging to active trait are usually open minded and interact with others more frequently. Hence the openness and agreeableness scales of RobotA are higher than those of RobotB and these two higher scales

Table 4-3: Definition of personality scales using Big Five factors.

RobotA (Active trait)

RobotB (Passive pessimist)

Openness 1 0.3

Conscientiousness 0.5 0.5

Extraversion 0.1 0.1

Agreeableness 0.5 0.2

Neuroticism 0.1 0.3

(Pα, Pβ) (0.34, 0.24) (0.20, -0.07)

lead the personality parameters (P_α, P_β) to more positive tendency. Furthermore, a more passive pessimist has the tendency to experience negative thinking in general. Therefore the neuroticism factor of RobotB is higher than that of RobotA. The higher neuroticism factor of RobotB leads its personality more negative tendency on arousal (β axis). After trait values have been identified, the robot personality parameters (P_α, P_β) are determined by using (2.4) and (2.5). And the proposed robotic mood transition model is built accordingly.

To evaluate the effectiveness of the proposed emotional expression generation scheme based on individual personality, we conducted two sessions of experiments by using the artificial face as shown in Fig. 4-4(b). In the experiments, the same input sets were presented to RobotA and RobotB with the regulated user emotional intensities, respectively with above-mentioned conversation. The robotic mood states were observed as the same user spoke to RobotA and RobotB. Accordingly, the artificial face reacted with different facial expressions resulting from mood state transition. Table 4-4 and Table 4-5 list the calculated robotic mood states (RM_k) and simulated facial expressions corresponding to RobotA and RobotB respectively. Video clips of this experimental can be found in [85].

Figure 4-5 depicts the mood transition of RobotA as the above conversation was performed. The initial mood state of RobotA was set at neutral state (0.61,-0.47), referring to Fig. 2-2. The mood transition trajectories moved from the fourth quadrant to third, second and first quadrant in the end. The corresponding facial expressions varied from neutral (#1) to boredom (#2), sadness (#3), anger (#4), surprise (#5), happiness (#6) and excitement (#7) in the end. The sharp turning point (#5) in Fig. 4-5 indicates that RobotA recognized the subject’s emotional state varied rapidly from anger to happiness. Figure 4-6 shows the mood transition of RobotB as the same emotional conversation was performed. The initial mood state of RobotB was also set on neutral state. The corresponding facial expressions varied from neutral (#1) to sleepiness (#2, #3), boredom (#4), sadness (#5), boredom (#6) and then near neutral in the end. Compared with Fig. 4-5, the robotic mood transition of passive trait is

Table 4-4: Facial expressions for the RobotA.

Table 4-5: Facial expressions for the RobotB.

basically in the regions of boredom, sad and neutral emotion. It stayed almost destructive no matter what kind of the subject’s emotional states came into play. On the contrary, the robotic mood transition of active trait scattered in whole emotional space. These features manifest the difference in characters between active and passive traits. This experiment reveals that the proposed mood transition scheme is able to realize robotic emotional behavior with different personality trait. Video clips of the mood transition for RobotA and RobotB can be found in [86].

Fig. 4-5: Robotic mood transition of RobotA.

-1 -0.5 0 0.5 1

Fig. 4-6: Robotic mood transition of RobotB.

Figure 4-7 shows the variation of seven fusion weights while the subject uttered to RobotA. In the emotional conversation, the subject spoke seven dialogues as shown in Table 4-1. The corresponding fusion weights variations of these seven dialogues are shown by seven sectors in Fig. 4-7. In dialogue #1, the neutral facial expressions dominate the output behavior;

this is reasonable since the subject’s emotional state is neutral. In dialogue #2 and #3, the weights of sadness gradually increase while the transitions of subject’s emotional states are from neutral to sad. Next, the sad weight decreases and the surprise weight increases as the subject feels angry progressively (dialogue #4). In the meantime, the fear weight also increases to respond to the subject’s angry expression. After the subject turned to be happy, the surprise and fear weights decrease (dialogue #5) and happy weight increases to dominate the output behavior.

Figure 4-8 shows the variation of seven fusion weights as the subject uttered to RobotB with the same emotional conversation. In dialogue #3 and #4, the weights of sadness gradually increase while the transitions of subject’s emotional states are from neutral to sad and angry. After the subject’s emotional states become happiness, the sad weight decreases

0 5 10 15 20 25 30 35 40

Fig. 4-7: Weights variation for RobotA (active trait).

0 5 10 15 20 25 30 35 40 0

0.2 0.4 0.6 0.8

1 Neutral

Happiness Surprise Fear Sadness Disgust Angry

#1 #2 #3 #4 #5 #6 #7

Time (sec) Weight

Fig. 4-8: Weights variation for RobotB (passive trait).

(dialogue #5) and neutral weight increases to dominate the output behavior. Compared with RobotA in Fig. 4-7, the personality of passive trait leads to less behavior variations and gets into sadness emotion easily although the subject’s emotional states become happiness. These features match the emotional tendency for both active and passive traits.