Motion Pattern Generation Research Articles

Likelihood-constrained coupled space learning for motion synthesis

When considering the human skeleton as a hierarchical structure (i.e. an upside-down tree structure), human motion can be treated as the rotation movements of joints around their parent joints, such that a motion sequence can be represented by the joint angle changing processes (referred to as motion patterns) of the joints between its start and end frames. With this motion representation mode, we convert motion synthesis to a motion pattern generation task, given a start-end frame pair. Conforming to such a motion synthesis scheme, we propose a coupled space learning model within which we enforce the sparse representation of the start-end frame pair on the motion frame dictionary to reconstruct its corresponding motion patterns well with a motion pattern dictionary. In addition, as motion naturalness is an important evaluation metric of motions, we propose to use the likelihood of a motion with a pre-estimated motion probability distribution to measure its naturalness and add the motion naturalness measurement into the coupled space learning model to constrain the generated motion patterns to reconstruct highly natural motions. The superior performances of both accuracy and naturalness of the synthesized motions demonstrate the effectiveness of the coupled space learning with the motion likelihood constraint.

An Automatic Detection of Hemorrhages in Retinal Fundus Images by Motion Pattern Generation

Programmed retinal picture investigation is a significant screening device for simple identification of eye infections like diabetic retinopathy, glaucoma. Hemorrhage (HE) identification is one of the significant strides in programmed extraction in Diabetic Retinopathy (DR) ailment. The manual strategy evaluated by clinicians is a tedious and asset concentrated procedure. Programmed retinal picture examination gives a prompt recognition and portrayal of retinal highlights preceding a pro investigation. Current HE detection techniques suffer from impractically-high computation time. In this research work, presented a technique to automatically detect HE. This paper proposes an efficient motion pattern generation algorithm to detect HE. The novelty of this method is to reduce the dimensional space based on image resolutions thus, enhances to speedup of the HE detection. The proposed strategy was executed in MATLAB and assessed both ordinary and unusual retinal pictures utilizing openly accessible MESSIDOR dataset. The proposed strategy accomplished better execution estimates when contrast with other cutting edge strategies. This automated method helps ophthalmologists in the screening process of DR.

Motion Pattern Generation of Peristaltic Type Robot by Numerical Optimization

Motion Pattern Generation of Peristaltic Type Robot by Numerical Optimization

A Tutorial for the Analysis of the Piecewise-Smooth Dynamics of a Constrained Multibody Model of Vertical Hopping

Contradictory demands are present in the dynamic modeling and analysis of legged locomotion: on the one hand, the high degrees-of-freedom (DoF) descriptive models are geometrically accurate, but the analysis of self-stability and motion pattern generation is extremely challenging; on the other hand, low DoF models of locomotion are thoroughly analyzed in the literature; however, these models do not describe the geometry accurately. We contribute by narrowing the gap between the two modeling approaches. Our goal is to develop a dynamic analysis methodology for the study of self-stable controlled multibody models of legged locomotion. An efficient way of modeling multibody systems is to use geometric constraints among the rigid bodies. It is especially effective when closed kinematic loops are present, such as in the case of walking models, when both legs are in contact with the ground. The mathematical representation of such constrained systems is the differential algebraic equation (DAE). We focus on the mathematical analysis methods of piecewise-smooth dynamic systems and we present their application for constrained multibody models of self-stable locomotion represented by DAE. Our numerical approach is demonstrated on a linear model of hopping and compared with analytically obtained reference results.

Human-Robot Interaction Design Using Smart Device Based Robot Partner

Nowadays, various robot partners have been developed to realize human-friendly interactions. In general, a robot system is composed of hardware modules, software modules, and application contents. It takes much time to design utterance contents and motion patterns as application contents simultaneously, but the design support systems mainly focus on the generation of robot motion patterns. Furthermore, a methodology is needed to easily change the specification of hardware and software according to diversified needs, and the developmental environment to design the application contents on verbal and nonverbal communication with people. In this paper, the authors propose robot partners with the modularized architecture of hardware and software by using smart devices, and propose a developmental environment to realize easy contents design of verbal and nonverbal communication. In order to solve the problem of difficulty in the content design, they develop a design support environment using design templates of communication application contents. Next, they apply the robot partner to navigate visitors to the robot contest of the system design forum held in Tokyo Metropolitan University. Finally, they show several examples of the interaction cases, and discuss the interaction design for smart device based robot partners.

スイング系運動における先端リンクの瞬間的高運動エネルギ状態創出のための自由振動に基づく運動パターン生成

スイング系運動における先端リンクの瞬間的高運動エネルギ状態創出のための自由振動に基づく運動パターン生成

Statistical mutual conversion between whole body motion primitives and linguistic sentences for human motions

This paper describes a novel approach to linguistic mutual inference, which enables robots not only to linguistically interpret the motion patterns in the form of sentences but also to generate the motions from the sentences. The inference can be established based on two modules, the motion language model and the natural language model. The motion language model stochastically represents an association structure between symbols of motion patterns and the words in sentences assigned to the motion. This is a statistical model with a three layered structure of motion symbols, latent states and words. The natural language model statistically represents a structure of sentences based on word bigrams. The motion language model and the natural language model correspond to semantics and syntax respectively. An approach to the integration of motion language model with the natural language model allows the linguistic mutual inference for the robots. The two kinds of inference can be made by solving search problems, search for a sequence of words corresponding to a motion and search for a symbol of motion pattern corresponding to a sentence. The proposed approach to interpretation of motion patterns as sentences and generation of motion patterns from the sentences through the integration of motion language model with the natural language model is validated by an experiment on the human behavioral data.

2P1-F08 先端リンクの瞬間的な高エネルギ状態創出のための自由振動理論に基づく運動パターン生成

2P1-F08 先端リンクの瞬間的な高エネルギ状態創出のための自由振動理論に基づく運動パターン生成

Extracting Postural Synergies for Robotic Grasping

We address the problem of representing and encoding human hand motion data using nonlinear dimensionality reduction methods. We build our work on the notion of postural synergies being typically based on a linear embedding of the data. In addition to addressing the encoding of postural synergies using nonlinear methods, we relate our work to control strategies of combined reaching and grasping movements. We show the drawbacks of the (commonly made) causality assumption and propose methods that model the data as being generated from an inferred latent manifold to cope with the problem. Another important contribution is a thorough analysis of the parameters used in the employed dimensionality reduction techniques. Finally, we provide an experimental evaluation that shows how the proposed methods outperform the standard techniques, both in terms of recognition and generation of motion patterns.

2P1-L07 運動低次元化を用いた多自由度ロボットの力学的拘束を満たす運動パターン生成(インフォマティブ・モーションとモーション・メディア-ロボットの身体性と運動-)

This paper proposes a motion pattern generator using dimensional reduction. A human motion pattern is measured by a motion capture system, and it is projected onto a low dimensional space based on principal component analysis. The low dimensional motion pattern is modified, so that the high dimensional motion pattern satisfies the motion condition, the dynamical consistency and the joint angle or torque limitation. The proposed method is applied to the motion pattern generation of the planar bipedal robot. The moon-walk performed by a human is transformed to the robot motion pattern. The ground contact states of foot links and a kinematic closed loop condition are given as the motion condition, and a floor reaction force condition is given for the dynamical consistency.

G151014 2足ヒューマノイドロボットの歩行安定化制御及びパターン生成([G151-01]ロボティクスメカトロニクス部門,一般セッション(1))

This paper describes a whole motion pattern generation based on the preview control.Also,a walking stability control which consists of ZMP compensatory control and inverted pendulum control for a biped humanoid robot to walk.The ZMP compensatory control updates the gravity position of the waist according to ZMP,the inverted pendulum control accelerates ZMP to the falling direction of the robot.In order to verify the effectiveness of the pattern generation and the control method,the simulation of the whole body motion is conducted by using Adams and MATLAB/Simulink.

Conceptual Imitation Learning in a Human-Robot Interaction Paradigm

In general, imitation is imprecisely used to address different levels of social learning from high-level knowledge transfer to low-level regeneration of motor commands. However, true imitation is based on abstraction and conceptualization. This article presents a model for conceptual imitation through interaction with the teacher to abstract spatio-temporal demonstrations based on their functional meaning. Abstraction, concept acquisition, and self-organization of proto-symbols are performed through an incremental and gradual learning algorithm. In this algorithm, Hidden Markov Models (HMMs) are used to abstract perceptually similar demonstrations. However, abstract (relational) concepts emerge as a collection of HMMs irregularly scattered in the perceptual space but showing the same functionality. Performance of the proposed algorithm is evaluated in two experimental scenarios. The first one is a human-robot interaction task of imitating signs produced by hand movements. The second one is a simulated interactive task of imitating whole body motion patterns of a humanoid model. Experimental results show efficiency of our model for concept extraction, proto-symbol emergence, motion pattern recognition, prediction, and generation.

A new approach on human–robot collaboration with humanoid robot RH-2

SUMMARYThis paper presents a novel control architecture for humanoid robot RH-2. The main objective is that a robot can perform different tasks in collaboration with humans in working environments. In order to achieve this goal, two control loops have to be defined. The outer loop, called collaborative control loop, is devoted to the generation of stable motion patterns for a robot, given a specific manipulation task. The inner loop, called posture stability control loop, acts to guarantee the stability of humanoid for different poses determined by motion patterns. A case study is presented in order to show the effectiveness of the proposed control architecture.

A biped static balance control and torque pattern learning under unknown periodic external forces

This paper addresses a biped balancing task in which an unknown external force is exerted, using the so-called ‘ankle strategy’ model. When an external force is periodic, a human adaptively maintains the balance, next learns how much force should be produced at the ankle joint from its repeatability, and finally memorized it as a motion pattern. To acquire motion patterns with balancing, we propose a control and learning method: as the control method, we adopt ground reaction force feedback to cope with an uncertain external force, while, as the learning method, we introduce a motion pattern generator that memorizes the torque pattern of the ankle joint by use of Fourier series expansion. In this learning process, the period estimation of the external force is crucial; this estimation is achieved based on local autocorrelation of joint trajectories. Computer simulations and robot experiments show effective control and learning results with respect to unknown periodic external forces.

Konservative Hämophiliebehandlung unter Berücksichtigung der Optimierung und Standardisierung

SummaryRehabilitation and physical therapy in the sense of functional health is based on the international classification of function. It takes in two considerations: function and structure of the body and their influence on personal and social activity. The integrative concept of joint function translates the basic concept of body function and structure on to the motion of the locomotive system. Stability needs motoric control. Motoric control and the integrated neural components are to be influenced through regulation of muscle tonus (massage, manual therapy, medical training therapy, electrotherapy and thermotherapy). The stability of the joint is controlled by the passive components. Passive structures are optimised through passive therapies like joint mobilisation. Active components of joint function are optimised through activation (medical training therapy, stabilisation, mono or multisegmental levels). Emotional and neuronal components can be triggered through kinesthetic exercises like PNF, Jacobsen relaxation, biofeedback training, mental training. Exact examination of the locomotive system will help finding all symptoms. This is how we individualise the therapy of symptoms and structures. The motion pattern generator shows us how to use the possibilities of functional influence on the motion pattern. We have a lot of afferent signals that need individualised functional therapy. This is why we need functional measurements like motion analysis on the basis of ultrasound. An other tool is the kinetic superficial EMG measurement of muscle function. We can use it to determine the status of the joint and it will lead to therapeutical decisions. All functional measurements will help to improve quality control of the physical therapy process. Even if the haemophilic patient is healthy he is not fit at all. Measurements of fitness will help us to improve special skills and establish the human being as a subject in society and environment. The main skill to be improved in haemophiliacs is coordination, strength of the stomach muscles and the vastus medialis and the flexibility of the hamstrings.

2P1-K09 ヒューマノイドロボットの動作パターンの抽象化と生成を目的とした砂時計型ネットの階層的統合モデル

ヒューマノイドロボットのハードウェア的技術開発の進歩に伴い, その大自由度な運動情報を取り扱う脳型情報処理系のソフトウェア開発への期待が高まっている。現在, 関節角センサ情報のうち主成分のみを抽出し運動パターンの分類をするなど情報の圧縮とその解析への研究が行われているが, 同時に実機へ再生するモデルまで実現した研究は少ない。本研究では多自由度ロボットにおける運動パターンの抽象化と生成を行うモデルとして, 複数の砂時計型のニューラルネットを階層的に構成する手法を提案し・実装結果を報告する。

1341 多リンク型流体内推進機構の高粘性流体中における運動パターン生成

1341 多リンク型流体内推進機構の高粘性流体中における運動パターン生成

2P1-B11 連想記憶による全身行動パターンの認識,記憶,生成(28. 創発ロボティクス)

2P1-B11 連想記憶による全身行動パターンの認識,記憶,生成(28. 創発ロボティクス)

F-0837 多リンク型流体内推進機構の運動パターンの生成(J24-1 バイオミメティクス)(J24 ヒトの運動機能(手腕系・下肢系,脊椎系,関節系など)/生物の運動機能(バイオミメティクス)とバイオロボット)

This paper describes the generation of motion pattern of a multi-link propulsion mechanism in fluid. To generate the motion pattern that has higher thrust force, a simple system imitating the oscillation of neurons and the Monte Carlo method for updating weights between adjacent neurons are employed. A bending motion similar to the bending of fish is generated for higher thrust force.

遺伝的アルゴリズムを用いた一次ホッピングロボットの最適跳躍パターン生成

遺伝的アルゴリズムを用いた一次ホッピングロボットの最適跳躍パターン生成