Human-like catching motion of humanoid using Evolutionary Algorithm(EA)-based imitation learning

Park, Ga-Ram; Kim, KangGeon; Kim, ChangHwan; Jeong, Mun-Ho; You, Bum-Jae; Ra, Syungkwon

doi:10.1109/roman.2009.5326070

Cited by 18 publications

(15 citation statements)

References 11 publications

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“…Therefore, the use of eigenvectors has to be changeable according to the properties of the data. To avoid this problem, many researchers have typically used a threshold fixed by a human expert Park et al 2009;Hueser et al 2006). For this paper, this problem was solved by choosing a reasonable dimension by investigating PCA dimensional space instead of the use of manually tuned dimensionality.…”

Section: Fig 13mentioning

confidence: 99%

Autonomous framework for segmenting robot trajectories of manipulation task

et al. 2014

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In manipulation tasks, motion trajectories are characterized by a set of key phases (i.e., motion primitives). It is therefore important to learn the motion primitives embedded in such tasks from a complete demonstration. In this paper, we propose a core framework that autonomously segments motion trajectories to support the learning of motion primitives. For this purpose, a set of segmentation points is estimated using a Gaussian Mixture Model (GMM) learned after investigating the dimensional subspaces reduced by Principal Component Analysis. The segmentation points can be acquired by two alternative approaches: (1) using a geometrical interpretation of the Gaussians obtained from the learned GMM, and (2) using the weights estimated along the time component of the learned GMM. The main contribution of this paper is the autonomous estimation of the segmentation points based on the GMM learned in a reduced dimensional space. The advantages of such an estimation are as follows: (1) segmentation points without any internal parameters to be manually predefined or pretuned (according to the types of given tasks and/or motion trajectories) can be estimated from a single training data, (2) segmentation points, in which non-linear motion trajectories can be better characterized than by using the original motion trajectories, can be estimated, and (3) natural motion trajectories can be retrieved by temporally rearranging motion segments. The capability of this autonomous segmentation framework is validated by four experiments. In the first experiment, motion segments are evaluated through a comparison with a human expert using a publicly available kitchen dataset. In the second experiment, motion segments are evaluated through a comparison with an existing approach using an open handwriting database. In the third experiment, the segmentation performance is evaluated by retrieving motion trajectories from the reorganization of motion segments. In the fourth experiment, the segmentation performance is evaluated by clustering motion segments.

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Section: Fig 13mentioning

confidence: 99%

Autonomous framework for segmenting robot trajectories of manipulation task

et al. 2014

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“…A body of work has been devoted to autonomous control of fast movements such as catching [3][4][5][6][7][8][9][10], dynamic re-grasping (throwing an object up and catching it) [11], hitting flying objects [12,13] and juggling [14][15][16]. Most approaches assumed a known model of the dynamics of motion and considered solely modeling the translational object motion.…”

Section: Robotic Catchingmentioning

confidence: 99%

“…For instance, Hong and Slotine [4] and Riley and Atkeson [7] model the trajectories of a flying ball as a parabola, and subsequently recursively estimate the ball's trajectory through least squares optimization. Frese et al [6], Bauml et al [10] and Park et al [9] also assume a parabolic form for the ball trajectories which they use in conjunction with an Extended Kalman filter [17] for on-line re-estimation. Ribnick et al [18] and Herrejon et al [19] estimate 3D trajectories directly from monocular image sequences based on the ballistic ball movement equation.…”

Section: Robotic Catchingmentioning

confidence: 99%

Estimating the non-linear dynamics of free-flying objects

Kim

Billard

2012

Robotics and Autonomous Systems

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a b s t r a c tThis paper develops a model-free method to estimate the dynamics of free-flying objects. We take a realistic perspective to the problem and investigate tracking accurately and very rapidly the trajectory and orientation of an object so as to catch it in flight. We consider the dynamics of complex objects where the grasping point is not located at the center of mass.To achieve this, a density estimate of the translational and rotational velocity is built based on the trajectories of various examples. We contrast the performance of six non-linear regression methods (Support Vector Regression (SVR) with Radial Basis Function (RBF) kernel, SVR with polynomial kernel, Gaussian Mixture Regression (GMR), Echo State Network (ESN), Genetic Programming (GP) and Locally Weighted Projection Regression (LWPR)) in terms of precision of recall, computational cost and sensitivity to choice of hyper-parameters. We validate the approach for real-time motion tracking of 5 daily life objects with complex dynamics (a ball, a fully-filled bottle, a half-filled bottle, a hammer and a pingpong racket). To enable real-time tracking, the estimated model of the object's dynamics is coupled with an Extended Kalman Filter for robustness against noisy sensing.

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“…A body of work has been devoted to autonomous control of fast movements such as catching [2] [3] [4] [5] [6] [7] and hitting flying objects [8] [9], or juggling [10] [11] [12] [13]. Next, we briefly review these works according to (1) how they predict trajectories of moving objects and (2) how they generate the robot's motions [14].…”

Section: Introductionmentioning

confidence: 99%

“…Hong et al [2] and Riley et al [5] model trajectories of the flying ball as a parabola, and subsequently recursively estimate the ball's trajectory through least squares optimization. Frese et al [4] and Park et al [7] also assume a parabolic form for the ball trajectories and predict the latter with Extended Kalman Filters [15]. Hong et al [2] generate trajectories of specific light-weighted objects using a generic aerodynamical model.…”

Section: Introductionmentioning

confidence: 99%

Learning motion dynamics to catch a moving object

Kim

Gribovskaya

Billard

2010

2010 10th IEEE-RAS International Conference on Humanoid Robots

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Abstract-In this paper, we consider a novel approach to control the timing of motions when these are encoded with autonomous dynamical systems(DS). Accurate timing of motion is crucial if a robot must synchronize its movement with that of a fast moving object. In previous work of ours [1], we developed an approach to encode robot motion into DS. Such a timeindependent encoding is advantageous in that it offers robustness against violent perturbation by adapting on the fly the trajectory while ensuring high accuracy at the target. We propose here an extension of the system that allows to control the timing of the motion while still benefitting from all the robustness properties deriving from the time-independent encoding of the DS. We validate the approach in experiments where the iCub robot learns from human demonstrations to catch a ball on the fly.

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Human-like catching motion of humanoid using Evolutionary Algorithm(EA)-based imitation learning

Cited by 18 publications

References 11 publications

Autonomous framework for segmenting robot trajectories of manipulation task

Autonomous framework for segmenting robot trajectories of manipulation task

Estimating the non-linear dynamics of free-flying objects

Learning motion dynamics to catch a moving object

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