In this paper, we introduce a framework for learning biped locomotion using dynamical movement primitives based on non-linear oscillators. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like locomotion. We suggest dynamical movement primitives as a central pattern generator (CPG) of a biped robot, an approach we have previously proposed for learning and encoding complex human movements. Demonstrated trajectories are learned through movement primitives by locally weighted regression, and the frequency of the learned trajectories is adjusted automatically by a novel frequency adaptation algorithm based on phase resetting and entrainment of coupled oscillators. Numerical simulations and experimental implementation on a physical robot demonstrate the effectiveness of the proposed locomotion controller.

In this paper we describe a learning framework for a central pattern generator (CPG)-based biped locomotion controller using a policy gradient method. Our goals in this study are to achieve CPG-based biped walking with a 3D hardware humanoid and to develop an efficient learning algorithm with CPG by reducing the dimensionality of the state space used for learning. We demonstrate that an appropriate feedback controller can be acquired within a few thousand trials by numerical simulations and the controller obtained in numerical simulation achieves stable walking with a physical robot in the real world. Numerical simulations and hardware experiments evaluate the walking velocity and stability. The results suggest that the learning algorithm is capable of adapting to environmental changes. Furthermore, we present an online learning scheme with an initial policy for a hardware robot to improve the controller within 200 iterations.

Biological systems seem to have a simpler but more robust locomotion strategy than existing biped walking controllers for humanoid robots. We show that a humanoid robot can step and walk using simple sinusoidal desired joint trajectories with their phase adjusted by a coupled oscillator model. We use the center of pressure location and velocity to detect the phase of the lateral robot dynamics. This phase information is used to modulate the desired joint trajectories. We do not explicitly use dynamical parameters of the humanoid robot. We hypothesize that a similar mechanism may exist in biological systems. We applied the proposed biologically inspired control strategy to our newly developed human-sized humanoid robot CB and a small size humanoid robot, enabling them to generate successful stepping and walking patterns.

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We show that a humanoid robot can step and walk using simple sinusoidal desired joint trajectories with their phase adjusted by a coupled oscillator model. We use the center of pressure location and velocity to detect the phase of the lateral robot dynamics. This phase information is used to modulate the desired joint trajectories. We applied the proposed control approach to our newly developed human sized humanoid robot and a small size humanoid robot developed by Sony, enabling them to generate successful stepping and walking patterns.

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In this paper, we discuss development of a sprawling-type quadruped robot named TITAN-XIII which is capable of high speed and energy efficient walking. We consider a sprawling-type quadruped robot is practical, because of its high stability which comes from the large supporting leg polygon and the low center of gravity. However in previous researches, the speed and the energy efficiency of a sprawling-type quadruped robot is lower than a mammal-type quadruped robot. Since cost of transport (COT) can be reduced by increase of walking velocity, we decided to design a fast walking sprawling-type quadruped robot. As a demonstrator, we developed the sprawling-type quadruped robot named TITAN-XIII. For a lightweight and compact leg, the right-angle type wire driven mechanism is adopted to the robot. To confirm its performance, several experiments were carried out and the robot walked at 1.38 m/s and COT of 1.76 was achieved. Finally, we compared the performance of TITAN-XIII with other quadruped robots, and confirm that its performance is almost same level as mammal-type quadruped robots.

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.