Walking is the most common terrestrial form of locomotion in animals. Its great versatility and flexibility has led to many attempts at building walking machines with similar capabilities. The control of walking is an active research area both in neurobiology and robotics, with a large and growing body of work. This paper gives an overview of the current knowledge on the control of legged locomotion in animals and machines and attempts to give walking control researchers from biology and robotics an overview of the current knowledge in both fields. We try to summarize the knowledge on the neurobiological basis of walking control in animals, emphasizing common principles seen in different species. In a section on walking robots, we review common approaches to walking controller design with a slight emphasis on biped walking control. We show where parallels between robotic and neurobiological walking controllers exist and how robotics and biology may benefit from each other. Finally, we discuss where research in the two fields diverges and suggest ways to bridge these gaps.

In this paper we describe an event-based walking control system for biped robots. Classically, the control of biped robots has been separated into walking pattern generation and stabilizing control. While this is an effective strategy in wellknown environments, walking in rough, unmodelled terrain can easily destabilize the system. Findings from neurobiology suggest that gait generation in animals and humans is strongly linked to sensory signals indicating certain events in the gait cycle. We therefore propose an event-based controller that triggers phase transitions based on the sensed walking state instead of only relying on time-based reference trajectories. We have implemented the proposed approach on our humanoid robot Lola. We present simulations and experimental results demonstrating the effectiveness of the approach when walking over unknown obstacles.

Abstract-The ability to avoid collisions is crucial for locomotion in cluttered environments. It is not enough to plan collision-free movements in advance when the environment is dynamic and not precisely known. We developed a new method which generates locally optimized trajectories online during the feedback control in order to dynamically avoid obstacles. This method successfully combines a local potential field method with a heuristic based on height and width of an obstacle to avoid collisions. The program's main feature is the integration of obstacles into the framework designed for self-collision avoidance presented in [1] and the collisions avoidance in taskspace. We show experimental results validating the method.

Pushing, soft ground contact and other unknown large disturbances can cause humanoid robots to fall. In order to face such problems, an accurate and fast model of the robot is necessary to estimate the state at a certain time in the future using only current measurements. We approximate the robot by a three-mass model with two degrees of freedom. Unilateral compliant contacts and a closed-loop control similar to the one used on the real robot are included additionally. Utilizing this model it is possible to predict the robot's behavior and calculate compensating motions for different disturbance cases. Furthermore, it is possible to integrate the estimation results into the real-time control system of our biped LOLA. Simulation and experimental results demonstrate the effectiveness and the advantages of the proposed method.

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