6566549
Humanoids walk with feedforward dynamic pattern and feedback sensory reflection
Since a biped humanoid inherently suffers from instability and always risks tipping over, ensuring high stability and reliability of walking is one of the most important goals. The paper proposes a walk control consisting of a feedforward dynamic pattern and a feedback sensory reflex. The dynamic pattern is a rhythmic and periodic motion, which satisfies the constraints of dynamic stability and ground conditions, and is generated assuming that the models of the humanoid and the environment are known. The sensory reflex is a simple, but rapid motion programmed with respect to sensory information. The sensory reflex, we propose, consists of the body posture control, the actual ZMP (zero moment point) control, and the landing time control. With the dynamic pattern and the sensory reflex, it is possible for the humanoid to walk rhythmically and to adapt itself to environmental uncertainties. The effectiveness of our proposed method was confirmed by walk experiments of an actual 26 DOF humanoid on an unknown rough terrain and in the presence of disturbances.
9408297
Acquisition and embodiment of motion elements in closed mimesis loop
It is needed for humanoid to acquire not only just a trajectory but also aim of the behavior and symbolic information during behavior development. We (2001) have proposed the mimesis system as a framework of synchronous learning model for behavior acquisition and symbol emergence. However, the motion elements which are fundamental representation of behavior have stood on the unsuitable assumption that they are given without taking the robot embodiment and dynamics into consideration. In this paper, the design theory of motion elements with consideration of the embodiment are shown, and novel methods of realization of the mimesis for real humanoids is proposed.
26916794
Natural Policy Gradient Reinforcement Learning for a CPG Control of a Biped Robot
Motivated by the perspective that animals’ rhythmic movements such as locomotion are controlled by neural circuits called central pattern generators (CPGs), motor control mechanisms by CPG have been studied. As an autonomous learning framework for a CPG controller, we previously proposed a reinforcement learning (RL) method called the CPG-actor-critic method. In this article, we propose a natural policy gradient learning algorithm for the CPG-actor-critic method, and applied our RL to an automatic control problem by a biped robot simulator. Computer simulations show that our RL makes the biped robot walk stably on various terrain.
798006
Efficient parallel dynamics computation of human figures
An efficient parallel algorithm for forward dynamics computation of human figures is proposed. The algorithm is capable of handling any kinematic chains including structure-varying ones. The asymptotic complexity of the algorithm is O(N) in serial computation and O(log N) in parallel computation on O(N) processors for most practical kinematic chains. The idea is to assemble a kinematic chain by adding the joints one by one and compute the constraint forces at the new joints using the principle of virtual work. The parallelism of the algorithm can be adapted for parallel processing systems with any number of processors by simply changing the assembly order. Simulation examples on an 8-node cluster demonstrate the effectiveness of the algorithm.
2052410
Reinforcement Learning for CPG-Driven Biped Robot
Animal's rhythmic movements such as locomotion are considered to be controlled by neural circuits called central pattern generators (CPGs). This article presents a reinforcement learning (RL) method for a CPG controller, which is inspired by the control mechanism of animals. Because the CPG controller is an instance of recurrent neural networks, a naive application of RL involves difficulties. In addition, since state and action spaces of controlled systems are very large in real problems such as robot control, the learning of the value function is also difficult. In this study, we propose a learning scheme for a CPG controller called a CPG-actor-critic model, whose learning algorithm is based on a policy gradient method. We apply our RL method to autonomous acquisition of biped locomotion by a biped robot simulator. Computer simulations show our method is able to train a CPG controller such that the learning process is stable.