Toward Robot Self-Consciousness (II): Brain-Inspired Robot Bodily Self Model for Self-Recognition

Abstract

The neural correlates and nature of self-consciousness is an advanced topic in Cognitive Neuroscience. Only a few animal species have been testified to be with this cognitive ability. From artificial intelligence and robotics point of view, few efforts are deeply rooted in the neural correlates and brain mechanisms of biological self-consciousness. Despite the fact that the scientific understanding of biological self-consciousness is still in preliminary stage, we make our efforts to integrate and adopt known biological findings of self-consciousness to build a brain-inspired model for robot self-consciousness. In this paper, we propose a brain-inspired robot bodily self model based on extensions to primate mirror neuron system and apply it to humanoid robot for self recognition. In this model, the robot firstly learns the correlations between self-generated actions and visual feedbacks in motion by learning with spike timing dependent plasticity (STDP), and then learns the appearance of body part with the expectation that the visual feedback is consistent with its motion. Based on this model, the robot uses multisensory integration to learn its own body in real world and in mirror. Then it can distinguish itself from others. In a mirror test setting with three robots with the same appearance, with the proposed brain-inspired robot bodily self model, each of them can recognize itself in the mirror after these robots make random movements at the same time. The theoretic modeling and experimental validations indicate that the brain-inspired robot bodily self model is biologically inspired, and computationally feasible as a foundation for robot self recognition.

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Notes

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    Robot Self-Consciousness Project: http://bii.ia.ac.cn/robot-self

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Corresponding author

Correspondence to Yi Zeng.

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Compliance with Ethical Standards

This study was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB02060007), and Beijing Municipal Commission of Science and Technology (Z161100000216124).

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Conflict of interests

All authors declare that they have no conflict of interest.

Additional information

Yi Zeng and Yuxuan Zhao have equal contribution to this work and should be regarded as co-first authors

Appendices

Appendix

Appendix A

Take one movement cycle as an example, as shown in Table 1. N denotes that the related brain regions are not functionally activated. If the value is 0, it means the value is too small and can be omitted.

Table 1 Time consumption of the experimental validation

During the learning phase, the motion sequences, the detected moving parts, and the visual inputs are used. The actual motion sequences and the detected motion sequences from vision are used for motor-visual associative learning, while the detected moving parts and the acquired visual inputs are used for appearance learning. The inputs for Fast R-CNN are images for left arm, and right arm of the robot itself, head of the self, and head of others (1000 pieces for each class, with different backgrounds and random movements). The training was conducted on GPU K40 for approximately 16 h.

Appendix B

In the multi-robots mirror self-recognition test, three robots made random movements facing a mirror. Since the three robots are making random movements at the same time, the robots will acquire all the angles for associative learning. As the times of movements grow, the robot will gradually identify which angle from vision are from itself. As an illustrative example, robot A is assigned to have repetitive movement with the angle value 88°, while the other two robots make random movements for 100 times respectively. The associative weights for motor angles (88°) and visual angles (0°–360°) are shown in Fig. 10. Based on Eq. 12, the angle with the maximum weight is 88°. At this time, when robot A generates a motion with 88°, it expect to detect 88° from vision.

Fig. 10
figure10

Weight distribution

There are errors for angle detection which are hard to be completely avoided. Hence, the detected visual angles may not always be the same when the motor angles are the same. We can identify from Fig. 10 that the detected visual angles are distributed in a small range. Hence, 𝜃 threshold are used to represent this range in Eq. 12 for self-recognition. Figure 11 presents the standard deviation between angles from vision and the expected angles, while the red line (with value 3.69) denotes the average value for the standard deviation from 1 to 360. Hence, as an approximation, 𝜃 threshold is set to 4° in Eq. 12. According to the time consumption of information acquisition for robots, t threshold is set to 2 s. Every movement cycle contains 20 motion points. Hence, in Eq. 12, n = 20-1.

Fig. 11
figure11

Standard deviation between predicted and actual visual angles

Appendix C

Figure 12 provides the belief values of different positions for each robot in nine independent tests. Each robot can pass the mirror test in all the nine tests.

Fig. 12
figure12

multi-robots mirror self-recognition test in nine times

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Zeng, Y., Zhao, Y., Bai, J. et al. Toward Robot Self-Consciousness (II): Brain-Inspired Robot Bodily Self Model for Self-Recognition. Cogn Comput 10, 307–320 (2018). https://doi.org/10.1007/s12559-017-9505-1

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Keywords

  • Robot self-consciousness
  • Robot bodily self model
  • STDP learning
  • Self-recognition