The versatile locomotion ability of quadruped animals has been widelyrecognized. Their movements show the impression of elegance includingadaptable, energy-efficient, and self-organized locomotion (Hoyt and Taylor,1981). Many quadruped robots have been developed to imitate their biologicalcounterparts (Raibert, 1986; Izumi et al., 2008; Fukuoka and Kimura, 2009;Wooden et al., 2010; Semini et al., 2011). There have been several biologicalevidences recorded over the years. These biological data identify key ingredientsunderlying locomotion of quadruped animals. The ingredients include centralpattern generators (CPGs), sensory feedback, vestibulospinal reflexes, andstepping reflexes (Boothe, 1999; Kimura et al., 2003; Kimura et al., 2007;Fukuoka and Kimura, 2009). Additionally, biological research found that acombination of CPGs and reflexes are crucial for successful quadruped locomotionin varied terrains (Manoonpong et al., 2007; Fukuoka and Kimura, 2009; Liu etal., 2018). However, quadruped robots’ levels of performance are still far fromthe natural ones. Moreover, the biological neural control mechanisms responsiblefor self-organized quadruped locomotion remain elusive, and the exact way ofhow the combination is achieved remains unknown. In fact, animal self-organized locomotion mechanisms seem to largely dependnot only on CPGs, reflexes, and sensory feedback but also on body-environmentinteraction (Owaki et al., 2012; Barikhan et al., 2014). They interact to generateemerging gaits with adaptability. Generally, CPGs organize basic rhythmicpatterns which are shaped and triggered by sensory feedback through reflexeswhile the body-environment interaction can form adaptive gaits to deal withchanges of the environment. By drawing lessons from the biological concepts andusing a modular artificial neural control approach (Hesse et al., 2012), wesimulated a mammal-like quadruped robot and used it as our experimentalplatform to investigate self-organized quadruped locomotion and body attitudestabilization under adaptive neural control and reflexes. In this study, self-organized locomotion and body attitude stabilization of thesimulated robot are achieved via a sensorimotor loop which involves adaptiveneural control, sensory feedback, and robot body dynamics (Fig. 1(A)). We proposed an adaptive neural control network (Fig. 1(B)) that can autonomouslygenerate self-organized emergent locomotion with adaptability for the robot. Thecontrol network consists of three main components: Decoupled neural centralpattern generator circuits, sensory feedback adaptation with dual-rate learning,and neural reflex mechanisms. Decoupled neural CPG circuits, based on neuraloscillators, produce rhythmic signals which are transmitted to motor neurons.Sensory feedback with adaptive factors and physical body-environmentinteraction adjusts the shapes and phases of the rhythmic signals and, thus,triggers the CPG circuits to form an adaptive gait. Neural reflexes, inspired by thevestibulospinal reflex, act as body attitude control to obtain dynamic stability ofthe robot on a tilting platform. Simulation results show that the robot canperform quadruped-like gaits in a self-organized manner and maintain its bodyattitude stability to deal with different tilting angles of the platform (Fig. 1(C, D)).In addition, this work also suggest that the combination of body-environmentinteraction and adaptive neural control is a powerful approach to solve self-organized quadruped locomotion and body attitude stabilization.