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We present an approach for kinesthetic teaching of motion primitives for a humanoid robot. The proposed teaching method allows for iterative execution and motion refinement using a forgetting factor. During the iterative motion refinement, a confidence value specifies an area of allowed refinement around the nominal trajectory. A novel method for continuous generation of motions from a hidden Markov...
This paper provides a new method for modeling, clustering, and generalizing complex pseudo-periodic motions in a Robot Programming by Demonstration (PbD) framework. Relevant features of the trajectories are extracted by applying a linear mapping off the surface part using Moving Window Principal Component Analysis. A Hidden Markov Model is used for segmentation and temporal clustering of feature data...
We present an approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) to learning robust models of human motion through imitation. The proposed approach allows us to extract redundancies across multiple demonstrations and build time-independent models to reproduce the dynamics of the demonstrated movements. The approach is systematically evaluated by using automatically generated...
In this paper, a novel 3-D motion trajectory signature is introduced to serve as an effective description to the raw trajectory. More importantly, based on the trajectory signature, a probabilistic model-based cluster signature is further developed for modeling a motion class. The cluster signature is a mixture model-based motion description that is useful for motion class perception, recognition...
A knowledge-based approach to imitation learning of motion generation for humanoid robots and an imitative motion generation system based on motion knowledge learning and reuse are described. The system has three parts: recognizing, learning, and modifying parts. The first part recognizes an instructed motion distinguishing it from the motion knowledge database by the hidden Markov model. When the...
In this paper, a novel method to synthesize a desired trajectory and sensory feedback control laws for robots based on the statistical characteristics of direct teaching data by a human is proposed. This work was motivated by a poor performance of an origami-folding robot developed by the authors. Since the robot simply replayed a given trajectory without sensory feedback control, it often failed...
Good communication among workers helps make comfortable atmosphere in an office and contributes to the increase company productivity. Therefore, relationship between workers is an important information the manager needs to know by examining the worker's comfort level in an office environment. Moreover, the quantified information of the human relationship is useful for robots to provide services to...
In this paper, we proposed a method to estimate the destination of walking persons from their walking patterns, for avoidance of collision accidents between pedestrians and robots in a human-robot coexistence environment. We adopted the hidden Markov model (HMM) as a model to represent walking patterns. We constructed a model for each movement pattern. A movement pattern was defined with a departure...
In this paper, we propose an imitation learning framework to generate multiple behaviors with balance control by recognizing human behaviors while estimating the ground reaction force. In our proposed method, a part of captured human motion data is recognized as one particular behavior that is represented by a linear dynamical model. Therefore, our method has small dependence on a classification criteria...
According to other expertspsila researches, it is necessary for the motion of service-robot manipulator should either be anticipated or be familiar to people. In order to realize this prospect, we propose that service-robot should imitate the motion of manipulator taught by people. In this paper we use the prototype of dual arm mobile service-robot FISR-1 (family intelligent service robot-1) to study...
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