Locomotion in animals provides a model for adaptive behavior as it is able to deal with various kinds of perturbations. Work in insects suggests that this evolved flexibility results from a modular architecture, which can be characterized by a recurrent neural network allowing for various emerging attractor states. Whereas a lower control-level coordinates joint movements on a short timescale, a higher-level handles action selection on longer timescales. Implementation of such a control system on a walking hexapod robot was able to deal with various walking patterns including disturbances such as uneven terrain or loss of a leg. Here, we propose a cognitive expansion to the adaptive control system that allows dealing with novel challenging situations. This approach makes use of an internal simulation-based planner that is triggered when the model-free controller fails to recover from an unstable pose. Using a grounded internal body model, the planner then tries, in internal simulation, different solutions out of context, and thus, proposes a new plan to be executed on the real robot. We demonstrate the feasibility of this control approach for walking over terrain with uncertain footholds in three scenarios.

Adapting the concept of continuous tracks that are propelled and guided by wheels, a self-propelled continuous-track-robot has been designed and built. The robot consists of actuated chain segments, thus enabling it to change its form, independent of guiding mechanisms. Using integrated sensors, the robot is able to adapt to the terrain and to overcome obstacles. This allows the robot to “roll” and climb in two dimensions. Possible extensions of the concept to three-dimensional navigation are presented as an outlook.

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 recruited in many tasks and can subserve in this way perception and cognition, it is important that they are grounded and embodied in sensorimotor representation. In this paper we analyze an internal model of the body and show how it can be used for motor control. We extend the Mean of Multiple Computation principle to a dual quaternion representation of transformation and show how this can be directly applied to the control of a simulated robot leg. The model is encoded as a recurrent neural network acting as an autoassociator that is able to solve any kinematic problem in an iterative fashion. We will analyze the convergence properties, especially when additional constraints (acting on the joint level) are introduced that restrict the attractor space.

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.

Biological systems are generally able to change joint stiffness by co-activation of antagonistic muscles. A close study of the stiffness generated in an antagonistically actuated, muscle-driven hinge joint has shown that in dependence of some muscle and joint parameters, positions in the angular joint space can be found for which different levels of co-activation do not generate any change in stiffness (stiffness nodes). Consequently, for joint positions in the vicinity of such a node, any stiffness controller is expected to fail. This paper proposes a novel stiffness node controller whose objective is to shift the stiffness node away when the joint approaches the angular position of the node. In order to test the ability of the node controller, a simulation of simultaneous torque and stiffness control is performed. In the simulation, two different cases will be examined. In the first case, the joint position is in the vicinity of a node and the node controller is switched off. In this situation the torque and stiffness controllers alone are not able to reach their desired values. In the second case the node controller is switched on which leads to a node displacement and - as a consequence - the controllers reach their requested values.