Neural Control with an Artificial Hormone System for EnergyEfficient Compliant Terrain Locomotion and Adaptation of Walking Robots In order to use walking robots for exploration in a real complex environment, an adaptive control system is required to allow them to successfully and efficiently traverse the terrains. To achieve this, we propose here our adaptive locomotion control technique of a walking robot. It is based on a modular structure, combining neural control with an artificial hormone system. The neural control coordinates all leg joints of the robot and generates its locomotion with various insectlike gaits. In parallel, the artificial hormone system uses the motor commands from the neural control and foot contact feedback to estimate the walking state and automatically adapt the joint movements with respect to the terrain. The adaptability is quickly achieved in an online manner within a few seconds. Robot walking experiments show that this adaptive control technique enables a sixlegged robot to adapt to various difficult terrains with energy efficiency. Such terrains include sand (loose ground), sponge with different softness levels (softcompliant ground), grass (vegetated ground), and floorpavement (hard ground). The technique does not require robot kinematics or an environmental model; and can, therefore, be potentially applied to different legged robots to achieve stable online adaptation and energyefficient locomotion on unpredictable (compliant) terrains.

Neural Control for Gait Generation and Adaptation of a Gecko Robot Geckos are highly adaptable creatures, able to scale a variety of slopes, including walls, and can change their gait depending on their environment. Roboticists have tried to implement this behaviour in gecko robots. So far, an openloop controlled robot without a tail that uses only one specific gait can climb to a 50xc2xb0 slope. In this paper, we propose neural control that allows a gecko robot to climb to a 70xc2xb0 slope by generating different gaits for various slope angles. The control consists of three main components: a central pattern generator (CPG) for generating various rhythmic patterns, CPG postprocessing for shaping the CPG signals, and a delay line for transmitting the shaped CPG signals to drive the legs of the gecko robot. The robot uses a body inclination sensor to provide sensory feedback for gait adaptation. When the incline is below 35xc2xb0, the robot walks with a predefined fast trot gait. If the incline is increased, it will change its gait from the trot gait to an intermediate gait, followed by a slow wave gait, which is both the most stable and the slowest gait, for climbing the steepest slopes. Using this walking strategy, the robot can efficiently climb a variety of slopes using different gaits and can automatically adapt its gait to maximise speed while ensuring stability.