Yasuo Kuniyoshi

RoboCup: The Robot World Cup Initiative

The Robot World Cup Initiative (R, oboCup) is attempt to foster AI and intelligent rohoties research by providing a standard problem where wide range of technologies especially concerning multi-agent research (:an be integrated and examined. The first RoboCup competition is to be, heht at. IJCAI-97, Nagoya. In order for a robot team to actually perform a soccer game. various technologies must I)e incorl)orated including: design principles of autononmus agents, multi-agent collaboration, strategy acquisition, real-time rea.~oning, robotics, and sensor-fllsion. Unlike AAAI robot competition, which is tuned for a single heavy-duty slow-moving robot. RoboCup is a task for a team of multiple f‘ast-moving robots under a dynamic environmen(. Although RoboCnp’s final target is a worhl cup with real robots, RoboCup offers a soft.ware platform for reseaxch on the software aspects of RoboCup. This paper describes teclini(’M challenges involw~d in RoboCup, rules, and simulation environment.

Learning by watching: extracting reusable task knowledge from visual observation of human performance

A novel task instruction method for future intelligent robots is presented, In our method, a robot learns reusable task plans by watching a human perform assembly tasks. Functional units and working algorithms for visual recognition and analysis of human action sequences are presented. The overall system is model based and integrated at the symbolic level. Temporal segmentation of a continuous task performance into meaningful units and identification of each operation is processed in real time by concurrent recognition processes under active attention control. Dependency among assembly operations in the recognized action sequence is analyzed, which results in a hierarchical task plan describing the higher level structure of the task. In another workspace with a different initial state, the system re-instantiates and executes the task plan to accomplish an equivalent goal. The effectiveness of our method is supported by experimental results with block assembly tasks. >

RoboCup: A Challenge Problem for AI

The Robot World-Cup Soccer (RoboCup) is an attempt to foster AI and intelligent robotics research by providing a standard problem where a wide range of technologies can be integrated and examined. The first RoboCup competition will be held at the Fifteenth International Joint Conference on Artificial Intelligence in Nagoya, Japan. A robot team must actually perform a soccer game, incorporating various technologies, including design principles of autonomous agents, multiagent collaboration, strategy acquisition, real-time reasoning, robotics, and sensor fusion. RoboCup is a task for a team of multiple fast-moving robots under a dynamic environment. Although RoboCups final target is a world cup with real robots, RoboCup offers a software platform for research on the software aspects of RoboCup. This article describes technical challenges involved in RoboCup, rules, and the simulation environment.

Cognitive Developmental Robotics: A Survey

Cognitive developmental robotics (CDR) aims to provide new understanding of how humans higher cognitive functions develop by means of a synthetic approach that developmentally constructs cognitive functions. The core idea of CDR is ldquophysical embodimentrdquo that enables information structuring through interactions with the environment, including other agents. The idea is shaped based on the hypothesized development model of human cognitive functions from body representation to social behavior. Along with the model, studies of CDR and related works are introduced, and discussion on the model and future issues are argued.

Cognitive developmental robotics as a new paradigm for the design of humanoid robots

This paper proposes cognitive developmental robotics(CDR) as a new principle for the design of humanoid robots. This principle may provide ways of understanding human beings that go beyond the current level of explanation found in the natural and social sciences. Furthermore, a methodological emphasis on humanoid robots in the design of artificial creatures holds promise because they have many degrees of freedom and sense modalities and, thus, must face the challenges of scalability that are often side-stepped in simpler domains. We examine the potential of this new principle as well as issues that are likely to be important to CDR in the future.

Online generation of humanoid walking motion based on a fast generation method of motion pattern that follows desired ZMP

This paper presents an efficient online method to generate humanoid walking motions that satisfy desired upper body trajectories while simultaneously carrying objects. A fast motion pattern generation technique that follows the desired ZMP is adopted. In order to satisfy the control input given online, subsequent motion patterns are updated and connected in a stable manner to the old ones while executing. During the creation of motion trajectories online, the commanded motion parameters are checked and modified automatically considering the performance limitations of the hardware. As an example application, we have implemented a one step cycle control system on the Humanoid H7. Experiments controlling the upper body motion and walking direction using a joystick interface are explained to demonstrate the validity of the proposed method.

Three dimensional bipedal stepping motion using neural oscillators-towards humanoid motion in the real world

CPG (central pattern generator) and entrainment dynamics together form a promising framework for robust and adaptive behavior generation for a high degree of freedom system in unstructured environment. This paper investigates its possibility in the domain of biped robotic locomotion. We extend a previous work on 2D biped locomotion using neural oscillators to 3D, introducing many more degrees of freedom and complexity in control. While the complexity of the problem has been increased, we have simplified the internal neural mechanism compared to the original 2D work. Our fully dynamic 3D simulation experiments showed that our mechanism can generate 3D stable biped stepping motion as well as tolerance against external perturbations.

RoboCup: A Challenge Problem for AI and Robotics

RoboCup is an attempt to foster AI and intelligent robotics research by providing a standard problem where wide range of technologies can be integrated and examined. The first RoboCup competition was held at IJCAI-97, Nagoya. In order for a robot team to actually perform a soccer game, various technologies must be incorporated including: design principles of autonomous agents, multi-agent collaboration, strategy acquisition, real-time reasoning, robotics, and sensorfusion. RoboCup is a task for a team of multiple fast-moving robots under a dynamic environment. Although RoboCups final target is a world cup with real robots, RoboCup offers a software platform for research on the software aspects of RoboCup. This paper describes technical challenges involved in RoboCup, rules, and simulation environment.

Early motor development from partially ordered neural-body dynamics: experiments with a cortico-spinal-musculo-skeletal model

Early human motor development has the nature of spontaneous exploration and boot-strap learning, leading to open-ended acquisition of versatile flexible motor skills. Since dexterous motor skills often exploit body-environment dynamics, we formulate the developmental principle as the spontaneous exploration of consistent dynamical patterns of the neural-body-environment system. We propose that partially ordered dynamical patterns emergent from chaotic oscillators coupled through embodiment serve as the core driving mechanism of such exploration. A model of neuro-musculo-skeletal system is constructed capturing essential features of biological systems. It consists of a skeleton, muscles, spindles, tendon organs, spinal circuits, medullar circuits (CPGs), and a basic cortical model. Through a series of experiments with a minimally simple body model, it is shown that the model has the capability of generating partially ordered behavior, a mixture of chaotic exploration and ordered entrained patterns. Models of self-organizing cortical areas for primary somatosensory and motor areas are introduced. They participate in the explorative learning by simultaneously learning and controlling the movement patterns. A scaled up version of the model, a human infant model, is constructed and put through preliminary experiments. Some meaningful motor behavior emerged including rolling over and crawling-like motion. The results show the possibility that a rich variety of meaningful behavior can be discovered and acquired by the neural-body dynamics without pre-defined coordinated control circuits.

METHODS FOR QUANTIFYING THE CAUSAL STRUCTURE OF BIVARIATE TIME SERIES

In the study of complex systems one of the major concerns is the detection and characterization of causal interdependencies and couplings between different subsystems. The nature of such dependencies is typically not only nonlinear but also asymmetric and thus makes the use of symmetric and linear methods ineffective. Moreover, signals sampled from real world systems are noisy and short, posing additional constraints on the estimation of the underlying couplings. In this article, we compare a set of six recently introduced methods for quantifying the causal structure of bivariate time series extracted from systems with complex dynamical behavior. We discuss the usefulness of the methods for detecting asymmetric couplings and directional flow of information in the context of uni- and bidirectionally coupled deterministic chaotic systems.