A novel approach to locomotion learning: Actor-Critic architecture using central pattern generators and dynamic motor primitives

Li, Cai; Lowe, Robert; Ziemke, Tom

doi:10.3389/fnbot.2014.00023

Cited by 16 publications

(11 citation statements)

References 48 publications

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“…robots) so that they may make sense of the patterns of sensorimotor activity that impact them (e.g. Li et al 2013Li et al , 2014. Affective states, grounded in dimensions of stimuli valuations, can take many forms -facial (Ekman 2003), prosodic (Schröder 2001), tactile (Andreasson et al 2018).…”

Section: Discussionmentioning

confidence: 99%

Bridging Connectionism and Relational Cognition through Bi-directional Affective-Associative Processing

Lowe

Almer

Balkenius

2019

Open Information Science

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Connectionist architectures constitute a popular method for modelling animal associative learning processes in order to glean insights into the formation of cognitive capacities. Such approaches (based on purely feedforward activity) are considered limited in their ability to capture relational cognitive capacities. Pavlovian learning value-based models, being not based purely on fully connected feedforward structure, have demonstrated learning capabilities that often mimic those of ‘higher’ relational cognition. Capturing data using such models often reveals how associative mechanisms can exploit structure in the experimental setting, so that ‘explicit’ relational cognitive capacities are not, in fact, required. On the other hand, models of relational cognition, implemented as neural networks, permit formation and retrieval of relational representations of varying levels of complexity. The flexible processing capacities of such models are, however, are subject to constraints as to how offline relational versus online (real-time, real-world) processing may be mediated. In the current article, we review the potential for building a connectionist-relational cognitive architecture with reference to the representational rank view of cognitive capacity put forward by Halford et al. Through interfacing system 1-like (connectionist/associative learning) and system 2-like (relational-cognition) computations through a bidirectional affective processing approach, continuity between Halford et al’s cognitive systems may be operationalized according to real world/online constraints. By addressing i) and ii) in this manner, this paper puts forward a testable unifying framework for system 1-like and system 2-like cognition.

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Section: Discussionmentioning

confidence: 99%

Bridging Connectionism and Relational Cognition through Bi-directional Affective-Associative Processing

Lowe

Almer

Balkenius

2019

Open Information Science

Self Cite

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“…Meanwhile, research on biology shows that human's rhythmic movement is the joint result of inherent patterns and reflex behavior [1,2]. Inspired by the CPG, a locomotion control strategy has been proven to be workable [3][4][5][6]. The rhythm signals can be produced endogenously by CPG, a kind of neural network.…”

Section: Introductionmentioning

confidence: 99%

Rhythmic-Reflex Hybrid Adaptive Walking Control of Biped Robot

Liu

Yang

et al. 2018

J Intell Robot Syst

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For the central pattern generation inspired biped walking control algorithm, it is hard to coordinate all the degrees of freedom of a robot by regulating the parameters of a neutral network to achieve stable and adaptive walking. In this work, a hybrid rhythmic-reflex control method is presented, which can realize stable and adaptive biped walking. By integrating zero moment position information, the walking stability can be improved on flat terrain. The robot's body attitude information is used to modulate the control system in real-time to realize sloped terrain adaptive walking. A staged parameter evolution process is used to derive the parameters. Through the entrainment of the oscillatory network and the feedback information, the real-time joint control signals can be regulated to realize adaptive walking. The presented control strategy has been verified by using a biped robot restricted in sagittal plane and the experiments reveal that the robot can successfully achieve changing sloped terrain adaptive walking.

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“…28 Besides, actor-critic RL combined with central pattern generators was used to generate bipedal locomotion patterns. 29…”

Section: Introductionmentioning

confidence: 99%

Adaptive stiffness control of passivity-based biped robot on compliant ground using double deep Q network

Yao

Guo

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part C:

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Passive dynamic walking exhibits human-like and energy-efficient gait. Biologically inspired compliance introduced to flexible passivity-based robot would be helpful to generate stable locomotion. However, designing adaptive controller for flexible biped on compliant ground still remains a challenge. This paper aims to design an adaptive and model-free stiffness controller for passivity-based flexible biped on compliant ground, where the hip stiffness is modulated by double deep Q network. One benefit of the double deep Q network is that adaptive stiffness control policy could be directly learned from inputs. At first, passive dynamic walking gait is utilized as a reference trajectory during double deep Q network training. Then the trained double deep Q network is used as adaptive stiffness controller for biped on compliant ground. Simulation results show that the passivity-based biped robot could walk in such walking cases as disturbed initial condition, level compliant ground, downslope slippery compliant surface, and varying compliance environments. The adaptive stiffness controller would be used to make the passivity-based biped robot adapt to the environmental changes.

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A novel approach to locomotion learning: Actor-Critic architecture using central pattern generators and dynamic motor primitives

Cited by 16 publications

References 48 publications

Bridging Connectionism and Relational Cognition through Bi-directional Affective-Associative Processing

Bridging Connectionism and Relational Cognition through Bi-directional Affective-Associative Processing

Rhythmic-Reflex Hybrid Adaptive Walking Control of Biped Robot

Adaptive stiffness control of passivity-based biped robot on compliant ground using double deep Q network

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