Adaptive Control Strategies for Interlimb Coordination in Legged Robots: A Review

Aoi, Shinya; Manoonpong, Poramate; Ambe, Yuichi; Matsuno, Fumitoshi; Wörgötter, Florentin

doi:10.3389/fnbot.2017.00039

Cited by 100 publications

(67 citation statements)

References 162 publications

(278 reference statements)

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“…Locomotion is a complex process that requires various components with real-time interaction between motor control functions and body dynamics through sensory feedback (embodied sensorimotor interaction) [ 13 ]. For example, it is known that stick insects do not generate coordinated motor outputs without sensory feedback [ 14 , 15 ], which indicates that sensory feedback plays a critical role in shaping these motor patterns.…”

Section: Introductionmentioning

confidence: 99%

Simple analytical model reveals the functional role of embodied sensorimotor interaction in hexapod gaits

et al. 2018

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Insects have various gaits with specific characteristics and can change their gaits smoothly in accordance with their speed. These gaits emerge from the embodied sensorimotor interactions that occur between the insect’s neural control and body dynamic systems through sensory feedback. Sensory feedback plays a critical role in coordinated movements such as locomotion, particularly in stick insects. While many previously developed insect models can generate different insect gaits, the functional role of embodied sensorimotor interactions in the interlimb coordination of insects remains unclear because of their complexity. In this study, we propose a simple physical model that is amenable to mathematical analysis to explain the functional role of these interactions clearly. We focus on a foot contact sensory feedback called phase resetting, which regulates leg retraction timing based on touchdown information. First, we used a hexapod robot to determine whether the distributed decoupled oscillators used for legs with the sensory feedback generate insect-like gaits through embodied sensorimotor interactions. The robot generated two different gaits and one had similar characteristics to insect gaits. Next, we proposed the simple model as a minimal model that allowed us to analyze and explain the gait mechanism through the embodied sensorimotor interactions. The simple model consists of a rigid body with massless springs acting as legs, where the legs are controlled using oscillator phases with phase resetting, and the governed equations are reduced such that they can be explained using only the oscillator phases with some approximations. This simplicity leads to analytical solutions for the hexapod gaits via perturbation analysis, despite the complexity of the embodied sensorimotor interactions. This is the first study to provide an analytical model for insect gaits under these interaction conditions. Our results clarified how this specific foot contact sensory feedback contributes to generation of insect-like ipsilateral interlimb coordination during hexapod locomotion.

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Section: Introductionmentioning

confidence: 99%

Simple analytical model reveals the functional role of embodied sensorimotor interaction in hexapod gaits

et al. 2018

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“…ER has motivated many di erent control systems for legged robots [4]. Arti cial Neural Networks (ANNs) have been used extensively because of their ability to represent complex functions, given the right evolved structure and connection weights [5,14,34].…”

Section: Controllers In Evolutionary Roboticsmentioning

confidence: 99%

Evolved embodied phase coordination enables robust quadruped robot locomotion

Nordmoen

Nygaard

Ellefsen

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference

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Overcoming robotics challenges in the real world requires resilient control systems capable of handling a multitude of environments and unforeseen events. Evolutionary optimization using simulations is a promising way to automatically design such control systems, however, if the disparity between simulation and the real world becomes too large, the optimization process may result in dysfunctional real-world behaviors. In this paper, we address this challenge by considering embodied phase coordination in the evolutionary optimization of a quadruped robot controller based on central pattern generators. With this method, leg phases, and indirectly also inter-leg coordination, are in uenced by sensor feedback. By comparing two very similar control systems we gain insight into how the sensory feedback approach a ects the evolved parameters of the control system, and how the performances di er in simulation, in transferal to the real world, and to di erent realworld environments. We show that evolution enables the design of a control system with embodied phase coordination which is more complex than previously seen approaches, and that this system is capable of controlling a real-world multi-jointed quadruped robot. The approach reduces the performance discrepancy between simulation and the real world, and displays robustness towards new environments.

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“…For instance, the differential oscillators 1 and 2 have N 1 = {2} and N 2 = {1, 3, 6, 8}, respectively, for the CPG network of the robot spider9. Subsequently, the output value of each differential oscillator can be calculated by Equation 3. To reduce the number of weights to be learned, we set w xioi = 1.0 in this work, i.e., the input of the out i -neuron equals the activation value x (i,t) of the x i -neuron.…”

Section: Robot Controllersmentioning

confidence: 99%

Directed Locomotion for Modular Robots with Evolvable Morphologies

Lan

Jelisavcic

Roijers

et al. 2018

Parallel Problem Solving From Nature – PPSN XV

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We generalize the well-studied problem of gait learning in modular robots in two dimensions. Firstly, we address locomotion in a given target direction that goes beyond learning a typical undirected gait. Secondly, rather than studying one fixed robot morphology we consider a test suite of different modular robots. This study is based on our interest in evolutionary robot systems where both morphologies and controllers evolve. In such a system, newborn robots have to learn to control their own body that is a random combination of the bodies of the parents. We apply and compare two learning algorithms, Bayesian optimization and HyperNEAT. The results of the experiments in simulation show that both methods successfully learn good controllers, but Bayesian optimization is more effective and efficient. We validate the best learned controllers by constructing three robots from the test suite in the real world and observe their fitness and actual trajectories. The obtained results indicate a reality gap that depends on the controllers and the shape of the robots, but overall the trajectories are adequate and follow the target directions successfully.North" or "go forward". To validate our results, we test the best controllers on real robots as well. The corresponding general problem statement is as follows:How to learn controllers efficiently for directed locomotion in a variety of modular robots with different morphologies?The paper addresses this problem by three specific research goals:1. Describe possible candidates, i.e., algorithms to learn controllers for directed locomotion in modular robots. 2. Evaluate these algorithms on a test suite of modular robots with different shapes and sizes by comparing their efficacy and efficiency in simulation. 3. Demonstrate the learned directed locomotion behavior on real robots and reflect on the reality gap in this application.

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Adaptive Control Strategies for Interlimb Coordination in Legged Robots: A Review

Cited by 100 publications

References 162 publications

Simple analytical model reveals the functional role of embodied sensorimotor interaction in hexapod gaits

Simple analytical model reveals the functional role of embodied sensorimotor interaction in hexapod gaits

Evolved embodied phase coordination enables robust quadruped robot locomotion

Directed Locomotion for Modular Robots with Evolvable Morphologies

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