36222597
Imitation and primitive symbol acquisition of humanoids by the integrated mimesis loop
Mimesis is a primitive learning framework and origins of human intelligence. We have developed a behavior acquisition and understanding system based on the mimesis. This system is able to abstract observed others' behaviors into conceptual symbols, to recognize others' behavior using the primitive symbols, and to generate self motion patterns using the primitive symbols. In this paper, we mention the integration of mimesis loop which is the acquisition and development system based on mimesis, and confirmation of the feasibility against whole body motions on virtual humanoids.
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.
14327674
Embodied Symbol Emergence Based on Mimesis Theory
“Mimesis” theory focused in the cognitive science field and “mirror neurons” found in the biology field show that the behavior generation process is not independent of the behavior cognition process. The generation and cognition processes have a close relationship with each other. During the behavioral imitation period, a human being does not practice simple joint coordinate transformation, but will acknowledge the parents’ behavior. It understands the behavior after abstraction as symbols, and will generate its self-behavior. Focusing on these facts, we propose a new method which carries out the behavior cognition and behavior generation processes at the same time. We also propose a mathematical model based on hidden Markov models in order to integrate four abilities: (1) symbol emergence; (2) behavior recognition; (3) self-behavior generation; (4) acquiring the motion primitives. Finally, the feasibility of this method is shown through several experiments on a humanoid robot.
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.
14381231
Polynomial design of the nonlinear dynamics for the brain-like information processing of whole body motion
For the development of an intelligent robot with many degrees-of-freedom, the reduction of the whole body motion and the implementation of the brain-like information system is necessary. We propose a reduction method of the whole body motion based on singular value decomposition and a design method of the brain-like information processing system using a nonlinear dynamics network with polynomial configuration. By using the proposed method, we design the humanoid whole body motion that is caused by the input sensor signals.
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.
16786247
Reinforcement learning for quasi-passive dynamic walking of an unstable biped robot
A class of biped locomotion called Passive Dynamic Walking (PDW) has been recognized to be efficient in energy consumption and a key to understand human walking. Although PDW is sensitive to the initial condition and disturbances, studies of Quasi-PDW which incorporates supplemental actuators have been reported to overcome this sensitivity. In this article, we propose a reinforcement learning method designed particularly for Quasi-PDW of a biped robot whose possession of knees makes the system unstable. Simulations show that the learning is quickly accomplished after 1000 episodes, and the obtained controller is robust against variations in the slope gradient and sudden perturbations.
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.