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The high complexity of the mechanical system and the difficult task of walking itself makes the task of designing the control for legged robots a diffcult one. Even if the implementation of parts of the desired functionality, like posture control or basic swing/stance movement, can be solved by the usage of classical engeneering approaches, the control of the overall system tends to be very unflexible. This paper introduces a new method to combine apects of classical robot control and behaviour based control. Inspired by the activation patterns in the brain and the spinal cord of animals we propose a behaviour network architecture using special signals like activity or target rating to influencce and coordinate the behaviours. The general concept of a single behaviour as well as their interaction within the network is described. This architecture is tested on the four-legged walking machine BISAM and experimental results are presented.

While the young Japanese monkey, , is growing, it can be trained operantly to maintain an upright posture and use bipedal (Bp) walking on a moving treadmill belt. For Bp locomotion, the animal generates sufficient propulsive force to smoothly and swiftly move the center of body mass (CoM) forward. The monkey can also adapt its gait to meet changing environmental demands. This appears to be accomplished by use of CNS strategies that include reactive and anticipatory control mechanisms. In this chapter, we provide evidence that the Bp walking monkey can select the most appropriate body-leg kinematic parameters to solve a variety of walking tasks. This recently developed non-human primate model has the potential to advance understanding of CNS operating principles that contribute to the elaboration and control of Bp walking in the human.

Given the continuous stream of movements that biological systems exhibit in their daily activities, an account for such versatility and creativity has to assume that movement sequences consist of segments, executed either in sequence or with partial or complete overlap. Therefore, a fundamental question that has pervaded research in motor control both in artificial and biological systems revolves around identifying movement primitives (a.k.a. units of actions, basis behaviors, motor schemas, etc.). What are the fundamental building blocks that are strung together, adapted to, and created for ever new behaviors? This paper summarizes results that led to the hypothesis of Dynamic Movement Primitives (DMP). DMPs are units of action that are formalized as stable nonlinear attractor systems. They are useful for autonomous robotics as they are highly flexible in creating complex rhythmic (e.g., locomotion) and discrete (e.g., a tennis swing) behaviors that can quickly be adapted to the inevitable perturbations of a dynamically changing, stochastic environment. Moreover, DMPs provide a formal framework that also lends itself to investigations in computational neuroscience. A recent finding that allows creating DMPs with the help of well-understood statistical learning methods has elevated DMPs from a more heuristic to a principled modeling approach. Theoretical insights, evaluations on a humanoid robot, and behavioral and brain imaging data will serve to outline the framework of DMPs for a general approach to motor control in robotics and biology.

Information at a distance provided by vision is critical for adaptive human locomotion. In this paper we focus on which visually observable environmental features from the visual images on the retina are extracted and how they are coupled to changes in appropriate locomotion patterns. Studies related to environmental features that pose a danger to the mobile agent are described: these include obstacles; sliding doors and undesirable foot landing area in the travel path. Both static and dynamic environmental features result in changing optic flow patterns: environmental features that change independently pose an added challenge. Key results from these studies are discussed in terms of issues that are important for the implementation of visually guided adaptable biped robot.