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This paper proposes an approach to hierarchy formation of human behaviors, extraction of the behavioral transitions, and their application to prediction and automatic generation of behaviors. Human demonstrator motion patterns are stored as motion symbols, which abstract the motion data by using Hidden Markov Models. The stored motion patterns are organized into a hierarchical tree structure, which...
This paper proposes a novel approach for extracting a model of movement primitives and their sequential relationships during online observation of human motion. In the proposed approach, movement primitives, modeled as hidden Markov models, are autonomously segmented and learned incrementally during observation. At the same time, a higher abstraction level hidden Markov model is also learned, encapsulating...
This paper describes an novel approach towards linguistic processing for robots through integration of a motion language module and a natural language module. The motion language module represents association between symbolized motion patterns and words. The natural language module models sentences. The motion language module and the natural language module are graphically integrated. The integration...
Since humanoid robots have similar body structures to humans, a humanoid robot is expected to perform various dynamic tasks including object manipulation. This research focuses on issues related to learning and performing object manipulation. Basic motion primitives for tasks are learned from observation of human's behaviors. An object manipulation task is divided into two types of motion primitives,...
In previous work, the authors have been developing a stochastic model based approach for on-line segmentation of whole body human motion patterns during human motion observation and learning, using a simplified kinematic model of the human body. In this paper, we extend the proposed approach to larger, more realistic kinematic models, which can better represent a larger variety of human motions. These...
This paper proposes a stochastic approach for representing and analyzing the gradual changes that occur in human movement during sports training. Human movement primitives are described using factorial hidden Markov models, and compared using the Kullback-Liebler distance, a measure of information divergence between two models. This representation is combined with an automated segmentation and clustering...
This paper proposes an on-line, interactive approach for incremental learning and visualization of full body motion primitives from observation of human motion. The human demonstrator motion is captured in a motion capture studio. The continuous observation sequence is first partitioned into motion segments, using stochastic segmentation. Motion segments are next incrementally clustered and organized...
This paper describes the linguistic model based on symbolization of motion patterns for humanoid robots. The model consists of two kinds of stochastic models : the motion language model and the natural language model. The motion language model stochastically connects the symbols of motion patterns to the morpheme words through the latent states which represent the underlying linguistic structure such...
In this paper, mimetic communication is extended to human-robot interaction tasks, in which physical contact transitions must be handled. The mimetic communication consists of imitation learning for learning low level motion primitives and a higher level interaction learning stage in which also the information about the human-robot contacts is included. For the imitation learning, Cartesian marker...
This paper describes the novel approach to integration of natural language processing and symbolization of motion patterns in order to allow for humanoid robotpsilas acquisition of language. This framework consists of two models : motion language model and natural language model. In the motion language model, morpheme words are stochastically associated with symbolized motion patterns via latent variables...
This paper describes an approach for on-line, incremental learning of full body motion primitives from observation of human motion. The continuous observation sequence is first partitioned into motion segments, using stochastic segmentation. Motion segments are next incrementally clustered and organized into a hierarchical tree structure representing the known motion primitives. Motion primitives...
This paper deals with the imitation of human motions by a humanoid robot based on marker point measurements from a 3D motion capture system. For imitating the humanpsilas motion, we propose a Cartesian control approach in which a set of control points on the humanoid is selected and the robot is virtually connected to the measured marker points via translational springs. The forces according to these...
This paper develops an approach for on-line segmentation of whole body human motion patterns during human motion observation and learning. A Hidden Markov Model is used to represent the incoming data sequence, where each model state represents the probability density estimate over a window of the data. Based on the assumption that data belonging to the same motion primitive will have the same underlying...
Since humanoid robots have similar body structures to humans, they are expected to perform various tasks including tool-use manipulation tasks instead of humans. This research studies on learning and performing tool-use manipulation tasks. For tool-use manipulations, understanding the relation between tool motion and whole body motion is crucial. In this paper, a tool-use motion model is designed...
This paper describes an imitative learning of driving time series data for intellectual cognition toward future automobiles. The driving pattern primitives consisting of states of the environment, vehicle and driver are symbolized by hidden Markov models (HMMs), which can be used for both recognition and generation of the driving patterns. The relationship among the HMMs can be represented by locating...
This paper proposes a method to recover missing data during observation by factorial hidden Markov models (FHMMs). The fundamental idea of the proposed method originates from the mimesis model, inspired by the mirror neuron system. By combining the motion recognition from partial observation algorithm and the proto-symbol based duplication of observed motion algorithm, whole body motion imitation...
This paper describes an improved methodology for human motion recognition and imitation based on factorial hidden Markov models (FHMM). Unlike conventional hidden Markov models (HMMs), FHMMs use a distributed state representation, which allows for more efficient representation of each time sequence. Once the FHMMs are trained with exemplar motion data, they can be used to generate sample trajectories...
This paper describes a novel algorithm for autonomous and incremental learning of motion pattern primitives by observation of human motion. Human motion patterns are abstracted into a hidden Markov model representation, which can be used for both subsequent motion recognition and generation, analogous to the mirror neuron hypothesis in primates. As new motion patterns are observed, they are incrementally...
Optical motion capturing systems are widely used to acquire human beings' motion patterns in humanoid imitation learning research. However, optical motion capturing systems have a restricted movable area. This paper proposes the HMM based mimesis scheme using a monocular camera mounted on a humanoid. This scheme releases the restriction of movable area and enables imitation in daily life environments...
Motion capture systems are used to obtain motion data such that humanoid robots or computer graphics (CG) characters can behave naturally. However, it has proven to be hard not only to modify the capture data without losing its reality but also to search for the required capture data in a lot of capture data. In this paper, we provide a solution to these problems based on our previous work on symbolization...
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