The main advantage of multi-legged robots is the ability to traverse uneven terrain and to overcome obstacles that would impede the movement of a wheeled robot. Compliant joint drives can further improve the performance of legged robot systems by considerably reducing the problems associated with uneven or slippery footholds. Small changes of the foot position in uneven terrain are compensated by the passive joint compliance without the need for fast contact detection and controller responses while the mechanical tension is reduced. On rougher terrain with bigger elevations/depressions of the surface structure, however, a reliable ground contact detection and stance height regulation for each leg is required to distribute the weight of the robot evenly among the legs on ground during stance phase. This work presents a concept that allows the inherently compliant hexapod robot HECTOR to walk on uneven terrain and to overcome moderate obstacles by means of a decentralized walking controller. An important prerequisite is the availability of inherently compliant joint drives with an increased power/weight-ratio.

While internal models are a prerequisite for higher-level function, they have to be grounded in lower-level function serving sensorimotor control. In this paper we introduce an internal body model for the control of a hexapod walker. The internal model deals with a highly complex robotic structure of 22 degrees of freedom and coordinates the single joint movements to achieve an overall stable and adaptive walking behavior. It is implemented as a hierarchical recurrent neural network consisting of different levels of abstraction which are tightly intertwined. We demonstrate the feasibility of the concept by applying the model to a simulated robot and show how the different levels of the body model interact and how this allows to scale the model even further. While the internal model is used in this context explicitly for motor control, it is also a predictive model and can be applied for sensor fusion. We discuss how in this way such an internal model offers the flexibility to be utilized in motor control and to be used for planning ahead by a cognitive expansion of the movement controller.

Walknet, a biological inspired neural network, has been proposed to control walking of a 6-legged; 18 DoF walker. Control of stance movement is difficult because all legs in stance are mechanically coupled. Based on the idea of velocity feedback, recently a specific revision, local positive velocity feedback, has been proposed to solve this problem using an extremely decentralised architecture. In this control scheme the locally measured joint velocity of an elastic joint is fed into the same joint in the next time step to maintain the movement. At the same time an additional part of the same joint controller observes the mechanical joint power to confine the positive feedback. In this paper, we show that this principle can actually be successfully applied to move a complete 6-legged system. This is tested using a dynamic simulation.

Starting from studies which revealed that positive feedback is found in the control system for walking in arthropods, we have constructed a new positive feedback driven joint that can be used for solving compliant motion tasks. We propose two different joint constructions each of which shows passive compliance. Based on these joints we introduce three different local positive velocity feedback (LPVF) controllers and discuss their properties in the context of motion generation in closed kinematic chains. The third circuit named undelayed dLPVF is used for the control of a compliant planar manipulator which turns a crank. Our concept is of highly decentralized nature and follows the idea of embodiment. In our case this means that a process which is controlled by LPVF controllers reveals its nature when the controllers interact with this process.