Abstract — The passive dynamic walker described in this paper is a robot with a minimal number of degrees of freedom, but which is still capable of stable 3D dynamic walking. First, we present the reduced-order dynamic models that we used to tune the characteristics of the robot’s gait. Then we present an actuated version of the robot and some preliminary active control strategies. The control problem for the actuated version of the robot is interesting because although it is theoretically challenging (4 degrees of under-actuation), the mechanical design of the robot made it relatively easy to create controllers which allowed the robot to walk stably on flat terrain and even up a small slope. I.

Abstract — Here we present the design of a passivedynamics based, fully autonomous, 3-D, bipedal walking robot that uses simple control, consumes little energy, and has human-like morphology and gait. Design aspects covered here include the freely rotating hip joint with angle bisecting mechanism; freely rotating knee joints with latches; direct actuation of the ankles with a spring, release mechanism, and reset motor; wide feet that are shaped to aid lateral stability; and the simple control algorithm. The biomechanics context of this robot is discussed in more detail in [1], and movies of the robot walking are available at Science Online and

In this paper, we present our design and experiments on a planar biped robot under the control of a pure sensor-driven controller. This design has some special mechanical features, for example small curved feet allowing rolling action and a properly positioned center of mass, that facilitate fast walking through exploitation of the robot’s natural dynamics. Our sensor-driven controller is built with biologically inspired sensor- and motor-neuron models, and does not employ any kind of position or trajectory tracking control algorithm. Instead, it allows our biped robot to exploit its own natural dynamics during critical stages of its walking gait cycle. Due to the interaction between the sensor-driven neuronal controller and the properly designed mechanics of the robot, the biped robot can realize stable dynamic walking gaits in a large domain of the neuronal parameters. In addition, this structure allows the use of a policy

RHex is a robotic hexapod with springy legs and just six actuated degrees of freedom. This work presents the development of a pronking gait for RHex, extending its efficiency and speed by exploiting its springy legs for hopping. A simple physical model of pronking is presented and verified qualitatively in simulation and experiment. In the course of analysing and attempting to stabilize pronking, the author develops an inertial attitude estimation system for RHex built around a three axis fibre-optic gyroscope. The author also validates a simple current estimation model for RHex’s motors, which is then used to detect leg touchdown during pronking. II Résumé RHex est un robot à six pattes dont chaque jambe élastique est contrôlée par un seul moteur, placé a la hanche. Cette thèse décrit le

Abstract — Humanoid research has made remarkable progress during the past 10 years. However, currently most humanoids use the target ZMP (Zero Moment Point) control algorithm for bipedal locomotion, which requires precise modeling and actuation with high control gains. On the contrary, humans do not rely on such precise modeling and actuation. Our aim is to examine biologically related algorithms for bipedal locomotion that resemble human-like locomotion. This paper describes an empirical study of a neural oscillator for the control of biped locomotion. We propose a new neural oscillator arrangement applied to a compass-like biped robot. Dynamic simulations and experiments with a real biped robot were carried out and the controller performs steady walking for over 50 steps. Gait variations resulting in energy efficiency was made possible through the adjustment of only a single neural activity parameter. Aspects of adaptability and robustness of our approach are shown by allowing the robot to walk over terrains with varying surfaces with different frictional properties. Initial results suggesting optimal amplitude for dealing with perturbation are also presented.

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We present velocity-based stability margins for fast bipedal walking that are sufficient conditions for stability, allow comparison between different walking algorithms, are measurable and computable, and are meaningful. While not completely necessary conditions, they are tighter necessary conditions than several previously proposed stability margins. The stability margins we present take into consideration a biped’s Center of Mass position and velocity, the reachable region of its swing leg, the time required to swing its swing leg, and the amount of internal angular momentum available for capturing balance. They predict the opportunity for the biped to place its swing leg in such a way that it can continue walking without falling down. We present methods for estimating these stability margins by using simple models of walking such as an inverted pendulum model and the Linear Inverted Pendulum model. We show that by considering the Center of Mass location with respect to the Center of Pressure on the foot, these estimates are easily computable. Finally, we show through simulation experiments on a 12 degree-of-freedom distributed-mass lower-body biped that these estimates are useful for analyzing and controlling bipedal walking. 2

Biped walking remains a difficult problem and robot models can greatly facilitate our understanding of the underlying biomechanical principles as well as their neuronal control. The goal of this study is to specifically demonstrate that stable biped walking can be achieved by combining the physical properties of the walking robot with a small, reflex based neuronal network, which is governed mainly by local sen-sor signals. Building on earlier work (Taga, 1995; Cruse et al., 1998), this study shows that human-like gaits emerge without specific posi-tion or trajectory control and that the walker is able to compensate small disturbances through its own dynamical properties. The re-flexive controller used here has the following characteristics, which are different from earlier approaches: (1) Control is mainly local. Hence, it uses only two signals (AEA=Anterior Extreme Angle and GC=Ground Contact) which operate at the inter-joint level. All other

Abstract — A number of conceptually simple but behaviorrich “inverted pendulum ” humanoid models have greatly enhanced the understanding and analytical insight of humanoid dynamics. However, these models do not incorporate the robot’s angular momentum properties, a critical component of its dynamics. We introduce the Reaction Mass Pendulum (RMP) model, a 3D generalization of the better-known reaction wheel pendulum. The RMP model augments the existing models by compactly capturing the robot’s centroidal momenta through its composite rigid body (CRB) inertia. This model provides additional analytical insights into legged robot dynamics, especially for motions involving dominant rotation, and leads to a simpler class of control laws. In this paper we show how a humanoid robot of general geometry and dynamics can be mapped into its equivalent RMP model. A movement is subsequently mapped to the time evolution of the RMP. We also show how an “inertia shaping” control law can be designed based on the RMP. I.

We explore the use of a shoe-mounted camera as a sensory system for wearable computing. We demonstrate tools useful for gait analysis, obstacle detection, and context recognition. Using only visual information, we detect periods of stability and motion during walking. In the stable phase, the foot can be assumed to be parallel to the ground plane. In this condition, the floor dominates the lower part of the camera’s view, and we show that it can be segmented out from the remainder of the scene, leaving walls and obstacles. We also demonstrate floor surface recognition for context awareness. 1.