Combining spiking motor primitives with a behaviourbased architecture to model locomotion for sixlegged robots Bioinspired robots take advantage of millions of years of evolution to provide interesting and flexible solutions for issues related to motion and perception. Often, they have challenging kinematics classical robotics control mechanisms are not always able to take advantage of them. A concrete example of this is LAURON V, a sixlegged robot for space exploration inspired by the stick insects. The main goals of this work is to combine classical behaviourbased control with motor primitives implemented with SNN for motion representation. We extend a previously presented bioinspired approach to represent hand and arm motion using motor primitives, and combine it with a behaviourbased architecture to model different locomotion behaviours for a multilegged robot. There are four main components. First, to model the individual leg motions we use two motor primitives implemented with spiking neural networks for the swing and stance phases. Second, to control the motor primitives of each leg there are local behaviours corresponding to each phase, and corresponding to each activation pattern. Third, the activation patterns are used to facilitate multileg coordination and generate different walking behaviours. Fourth, a highlevel control interface integrates control signals from other sources and activates the patterns. We conducted five different experiments to evaluate our approach in a simulated environment using the Neurorobotics Platform (NRP). The results show that our modelling approach with motor primitives is flexible enough to represent different types of motions, and also highlight the value of the NRP for robotics development.

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.