Locomotion Control With Frequency and Motor Pattern Adaptations

Abstract

Existing adaptive locomotion control mechanisms for legged robots are usually aimed at one specific type of adaptation and rarely combined with others. Adaptive mechanisms thus stay at a conceptual level without their coupling effect with other mechanisms being investigated. However, we hypothesize that the combination of adaptation mechanisms can be exploited for enhanced and more efficient locomotion control as in biological systems. Therefore, in this work, we present a central pattern generator (CPG) based locomotion controller integrating both a frequency and motor pattern adaptation mechanisms. We use the state-of-the-art Dual Integral Learner for frequency adaptation, which can automatically and quickly adapt the CPG frequency, enabling the entire motor pattern or output signal of the CPG to be followed at a proper high frequency with low tracking error. Consequently, the legged robot can move with high energy efficiency and perform the generated locomotion with high precision. The versatile state-of-the-art CPG-RBF network is used as a motor pattern adaptation mechanism. Using this network, the motor patterns or joint trajectories can be adapted to fit the robot's morphology and perform sensorimotor integration enabling online motor pattern adaptation based on sensory feedback. The results show that the two adaptation mechanisms can be combined for adaptive locomotion control of a hexapod robot in a complex environment. Using the CPG-RBF network for motor pattern adaptation, the hexapod learned basic straight forward walking, steering, and step climbing. In general, the frequency and motor pattern mechanisms complement each other well and their combination can be seen as an essential step toward further studies on adaptive locomotion control.

Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot

“…Previous results show that the integration between motor pattern mechanisms and adaptation with a CPG-RBF leads to locomotion control of a hexapod robot in a complex environment. This kind of frequency adaptation not only significantly reduces energy use but also is comparable to the biological behaviors observed in animal locomotion (Thor et al, 2021).…”

“…Architectures based on SNN found in the literature have solved decision-making and motor control tasks. As mentioned in Suzuki et al (2021), Thor et al (2021), and Pardo-Cabrera et al (2022), MFR models have been implemented to solve these tasks as well. The literature reviewed in this work shows that MFR models which satisfy the decision-making and controlmotor for mobile automata navigation have not been explored widely.…”

A gecko-inspired robot with CPG-based neural control for locomotion and body height adaptation

“…Thus, by using the versatile neural CPG model, we can use its embedded neurodynamics to generate more complex robot locomotion behaviour without using new oscillators. For the RBF network module, it can be extended by online learning methods to adapt the RBF output weights for online joint trajectory adaptation to deal with various (unpredictable) terrains as shown in [35,36]. Due to our modular structure, the neural CPG model can be replaced by other oscillator or CPG models and the feedforward RBF network can be replaced or combined with a reservoir computing-based recurrent neural network to obtain motor memory for robust locomotion [37].…”

NeuroVis: Real-Time Neural Information Measurement and Visualization of Embodied Neural Systems

“…It can be used to present novel neural systems and provide an explanation of such. For example, one can use NeuroVis to analyze and comprehend the underlying mechanisms of a neural system in order to 1) efficiently reduce/optimize its size, e.g., by removing unimportant (less active) neurons/connections (Han et al, 2015 ) and/or 2) efficiently scale it up by introducing new neural modules for new functions without destroying existing functions (Grinke et al, 2015 ; Thor et al, 2021 ). This will shape the way we build a neural system shifting from purely black box to white box or their combination toward explainable and understandable AI systems with trust and transparency (Loyola-Gonzalez, 2019 ).…”