Abstract
Many CPG-based locomotion models have a problem known as the tracking error problem, where the mismatch between the CPG driving signal and the state of the robot can cause undesirable behaviours for legged robots. Towards alleviating this problem, we introduce a mechanism that modulates the CPG signal using the robot’s interoceptive information. The key concept is to generate a driving signal that is easier for the robot to follow, yet can drive the locomotion of the robot. This can be done by nudging the CPG signal in the direction of lower tracking error, which can be analytically calculated. Unlike other reactive CPG, the proposed method does not rely on any parametric learning ability to adjust the shape of the signal, making it a unique option for a biological adaptive motor control. Our experiment results show that the proposed method successfully reduces the tracking error. We also show that the CPG signal, regulated by the proposed method, is robust to perturbation and can smoothly return back to the default pattern.
T. Chuthong and B. Leung—Equal contribution.
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Notes
- 1.
see also https://youtu.be/uMxDPPg1Q9A.
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Acknowledgement
We thank Mathias Thor for his technical support on the MORF robot simulation and acknowledge financial support by the VISTEC research grant on bioinspired robotics [P.M. (PI)] and in part by the NUAA Research Fund [P.M. (PI), P.N.].
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Chuthong, T., Leung, B., Tiraborisute, K., Ngamkajornwiwat, P., Manoonpong, P., Dilokthanakul, N. (2020). Dynamical State Forcing on Central Pattern Generators for Efficient Robot Locomotion Control. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12533. Springer, Cham. https://doi.org/10.1007/978-3-030-63833-7_67
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