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Relevant bibliographies by topics / Primitive motion

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Academic literature on the topic 'Primitive motion'

Author: Grafiati

Published: 5 June 2025

Last updated: 6 July 2025

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Journal articles on the topic "Primitive motion"

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Apostolopoulos, Sotiris, Marion Leibold, and Martin Buss. "Energy Efficient and Robust Balancing with Motion Primitive Switching." International Journal of Humanoid Robotics 14, no. 03 (2017): 1750009. http://dx.doi.org/10.1142/s0219843617500098.

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Balancing motions are usually designed using simplified models of the Center of Mass (CoM) and feedback control without accounting for energy efficiency. In order to tackle this shortcoming, we introduce a Motion Primitive switching methodology where samples of optimal motions (Motion Primitives) are chosen online based on a Euclidean distance metric. The chosen sample is used to provide reference trajectories, torques and ground reaction forces to be tracked. In order to satisfy all of the modeling assumptions while tracking the reference values, a Quadratic Program (QP) is solved online where the dynamics of the robot, friction, Center of Pressure and torque bounds are treated as constraints. Convergence to the desired trajectories is dictated by a Control Lyapunov Function constraint which is introduced in the QP. The methodology is evaluated on a four-link simulated robot where we show that switching between Motion Primitives provides energy efficient balancing motions for different disturbance situations. At the same time the methodology provides more efficient motions for different disturbance forces when compared to a nonswitching approach, where a Motion Primitive is chosen only once at the beginning.

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Li, Jiaxin, Hasiaoqier Han, Jinxin Hu, Junwei Lin, and Peiyi Li. "Robot Learning Method for Human-like Arm Skills Based on the Hybrid Primitive Framework." Sensors 24, no. 12 (2024): 3964. http://dx.doi.org/10.3390/s24123964.

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This paper addresses the issue of how to endow robots with motion skills, flexibility, and adaptability similar to human arms. It innovatively proposes a hybrid-primitive-frame-based robot skill learning algorithm and utilizes the policy improvement with a path integral algorithm to optimize the parameters of the hybrid primitive framework, enabling robots to possess skills similar to human arms. Firstly, the end of the robot is dynamically modeled using an admittance control model to give the robot flexibility. Secondly, the dynamic movement primitives are employed to model the robot’s motion trajectory. Additionally, novel stiffness primitives and damping primitives are introduced to model the stiffness and damping parameters in the impedance model. The combination of the dynamic movement primitives, stiffness primitives, and damping primitives is called the hybrid primitive framework. Simulated experiments are designed to validate the effectiveness of the hybrid-primitive-frame-based robot skill learning algorithm, including point-to-point motion under external force disturbance and trajectory tracking under variable stiffness conditions.

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Dong, Shuai, Zhihua Yang, Weixi Zhang, and Kun Zou. "Dynamic movement primitives based on positive and negative demonstrations." International Journal of Advanced Robotic Systems 20, no. 1 (2023): 172988062311529. http://dx.doi.org/10.1177/17298806231152997.

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Dynamic motion primitive has been the most prevalent model-based imitation learning method in the last few decades. Gaussian mixed regression dynamic motion primitive, which draws upon the strengths of both the motion model and the probability model to cope with multiple demonstrations, is a very practical and conspicuous branch in the dynamic motion primitive family. As Gaussian mixed regression dynamic motion primitive only learns from expert demonstrations and requires full environmental information, it is incapable of handling tasks with unmodeled obstacles. Aiming at this problem, we proposed the positive and negative demonstrations-based dynamic motion primitive, for which the introduction of negative demonstrations can bring additional flexibility. Positive and negative demonstrations-based dynamic motion primitive extends Gaussian mixed regression dynamic motion primitive in three aspects. The first aspect is a new maximum log-likelihood function that balances the probabilities on positive and negative demonstrations. The second one is the positive and negative demonstrations-based expectation–maximum, which involves iteratively calculating the lower bound of a new Q-function. And the last is the application framework of data set aggregation for positive and negative demonstrations-based dynamic motion primitive to handle unmodeled obstacles. Experiments on several typical robot manipulating tasks, which include letter writing, obstacle avoidance, and grasping in a grid box, are conducted to validate the performance of positive and negative demonstrations-based dynamic motion primitive.

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Wang, Zi, Caelan Reed Garrett, Leslie Pack Kaelbling, and Tomás Lozano-Pérez. "Learning compositional models of robot skills for task and motion planning." International Journal of Robotics Research 40, no. 6-7 (2021): 866–94. http://dx.doi.org/10.1177/02783649211004615.

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The objective of this work is to augment the basic abilities of a robot by learning to use sensorimotor primitives to solve complex long-horizon manipulation problems. This requires flexible generative planning that can combine primitive abilities in novel combinations and, thus, generalize across a wide variety of problems. In order to plan with primitive actions, we must have models of the actions: under what circumstances will executing this primitive successfully achieve some particular effect in the world? We use, and develop novel improvements to, state-of-the-art methods for active learning and sampling. We use Gaussian process methods for learning the constraints on skill effectiveness from small numbers of expensive-to-collect training examples. In addition, we develop efficient adaptive sampling methods for generating a comprehensive and diverse sequence of continuous candidate control parameter values (such as pouring waypoints for a cup) during planning. These values become end-effector goals for traditional motion planners that then solve for a full robot motion that performs the skill. By using learning and planning methods in conjunction, we take advantage of the strengths of each and plan for a wide variety of complex dynamic manipulation tasks. We demonstrate our approach in an integrated system, combining traditional robotics primitives with our newly learned models using an efficient robot task and motion planner. We evaluate our approach both in simulation and in the real world through measuring the quality of the selected primitive actions. Finally, we apply our integrated system to a variety of long-horizon simulated and real-world manipulation problems.

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Pedrosa, Matheus V. A., Patrick Scheffe, Bassam Alrifaee, and Kathrin Flaßkamp. "Optimization-based motion primitive automata for autonomous driving." at - Automatisierungstechnik 71, no. 4 (2023): 294–300. http://dx.doi.org/10.1515/auto-2022-0158.

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Abstract Trajectory planning for autonomous cars can be addressed by primitive-based methods, which encode nonlinear dynamical system behavior into automata. In this paper, we focus on optimal trajectory planning. Since, typically, multiple criteria have to be taken into account, multiobjective optimization problems have to be solved. For the resulting Pareto-optimal motion primitives, we introduce a universal automaton, which can be reduced or reconfigured according to prioritized criteria during planning. We evaluate a corresponding multi-vehicle planning scenario with both simulations and laboratory experiments.

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Dawood, Farhan, and Chu Kiong Loo. "Developmental Approach for Behavior Learning Using Primitive Motion Skills." International Journal of Neural Systems 28, no. 04 (2018): 1750038. http://dx.doi.org/10.1142/s0129065717500381.

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Imitation learning through self-exploration is essential in developing sensorimotor skills. Most developmental theories emphasize that social interactions, especially understanding of observed actions, could be first achieved through imitation, yet the discussion on the origin of primitive imitative abilities is often neglected, referring instead to the possibility of its innateness. This paper presents a developmental model of imitation learning based on the hypothesis that humanoid robot acquires imitative abilities as induced by sensorimotor associative learning through self-exploration. In designing such learning system, several key issues will be addressed: automatic segmentation of the observed actions into motion primitives using raw images acquired from the camera without requiring any kinematic model; incremental learning of spatio-temporal motion sequences to dynamically generates a topological structure in a self-stabilizing manner; organization of the learned data for easy and efficient retrieval using a dynamic associative memory; and utilizing segmented motion primitives to generate complex behavior by the combining these motion primitives. In our experiment, the self-posture is acquired through observing the image of its own body posture while performing the action in front of a mirror through body babbling. The complete architecture was evaluated by simulation and real robot experiments performed on DARwIn-OP humanoid robot.

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Yan, X. T., K. Case, and R. H. Weston. "A Generalized Approach to the Modelling of Modular Machines." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 208, no. 3 (1994): 191–203. http://dx.doi.org/10.1243/pime_proc_1994_208_078_02.

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This paper describes a method of graphically simulating modular machines within a computer aided design environment. This forms part of a much larger Science and Engineering Research Council (SERC) funded programme aimed at advancing modern practices when designing and building manufacturing machines. A generalized approach to the synthesis of the generic features of various kinematic motion pairs is presented and prismatic and revolute motion primitives generalized in their functional and geometric aspects. A hierarchical ring and tree data structure has been designed and implemented to comprehensively represent these motion pairs and to simulate their performance. More complex modular manufacturing machines can be represented using information from a library of up to three degree of freedom motion modules. Seven two degree of freedom motion primitives and twelve three degree of freedom motion primitives with articulation configurations have been analysed and included in the motion primitive library. The configuration of modular machines comprised of physically separate but logically connected distributed motion primitives are described. Examples of a two-finger industrial robot gripper and a three-finger industrial robot hand are used to demonstrate the general principles.

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Kulbacki, Marek, Bartosz Jablonski, Ryszard Klempous, and Jakub Segen. "Learning from Examples and Comparing Models of Human Motion." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 5 (2004): 477–81. http://dx.doi.org/10.20965/jaciii.2004.p0477.

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This paper addresses a problem of creating character animations from motion capture clips. The main problem we want to solve is partition set of primitive motions into appropriate groups according to similarity between motions. We construct motion models to easier extract features of given motions and make animation process more flexible. Using these models we propose measure of discrepancy between motions. Moreover it normalizes length of motions and decreases high dimension of considered motion data, so clustering may take place in dimensionally reduced space. In addition we examine different motion representations for the sake of the best clustering results.

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Nuevo-Gallardo, Cristina, José Emilio Traver, Inés Tejado, and Blas M. Vinagre. "Purcell’s Three-Link Swimmer: Assessment of Geometry and Gaits for Optimal Displacement and Efficiency." Mathematics 9, no. 10 (2021): 1088. http://dx.doi.org/10.3390/math9101088.

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This paper studies the displacement and efficiency of a Purcell’s three-link microswimmer in low Reynolds number regime, capable of moving by the implementation of a motion primitive or gait. An optimization is accomplished attending to the geometry of the swimmer and the motion primitives, considering the shape of the gait and its amplitude. The objective is to find the geometry of the swimmer, amplitude and shape of the gaits which make optimal the displacement and efficiency, in both an individual way and combined (the last case will be referred to as multiobjective optimization). Three traditional gaits are compared with two primitives proposed by the authors and other three gaits recently defined in the literature. Results demonstrate that the highest displacement is obtained by the Tam and Hosoi optimal velocity gait, which also achieves the best efficiency in terms of energy consumption. The rectilinear and Tam and Hosoi optimal efficiency gaits are the second optimum primitives. Regarding the multiobjective optimization and considering the two criteria with the same weight, the optimum gaits turn out to be the rectilinear and Tam and Hosoi optimal efficiency gaits. Thus, the conclusions of this study can help designers to select, on the one hand, the best swimmer geometry for a desired motion primitive and, on the other, the optimal method of motion for trajectory tracking for such a kind of Purcell’s swimmers depending on the desired control objective.

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ROS, E., F. J. PELAYO, D. PALOMAR, I. ROJAS, J. L. BERNIER, and A. PRIETO. "STIMULUS CORRELATION AND ADAPTIVE MOTION DETECTION USING SPIKING NEURONS." International Journal of Neural Systems 09, no. 05 (1999): 485–90. http://dx.doi.org/10.1142/s0129065799000526.

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Stimulus correlation and adaptive movement detection, among other tasks can be performed with VLSI general-purpose neurons that have controllable steady and transient responses. This paper presents experimental results of simple neural primitives based on the CMOS neuron approach described in [11]. Stimulus correlation experiments illustrate the well defined behavior of the CMOS approach. This basic primitive is used to implement motion detectors with adaptive capabilities that enable it to work efficiently in a wide velocity range.

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Dissertations / Theses on the topic "Primitive motion"

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Nordmann, Arne [Verfasser], and Jochen J. [Akademischer Betreuer] Steil. "Modeling of motion primitive architectures using domain-specific languages / Arne Nordmann ; Betreuer: Jochen J. Steil." Bielefeld : Universitätsbibliothek Bielefeld, 2016. http://d-nb.info/1117134954/34.

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Grymin, David J. "Two-Step System Identification and Primitive-Based Motion Planning for Control of Small Unmanned Aerial Vehicles." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/24520.

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This dissertation addresses motion planning, modeling, and feedback control for autonomous vehicle systems. A hierarchical approach for motion planning and control of nonlinear systems operating in obstacle environments is presented. To reduce computation time during the motion planning process, dynamically feasible trajectories are generated in real-time through concatenation of pre-specified motion primitives. The motion planning task is posed as a search over a directed graph, and the applicability of informed graph search techniques is investigated. Specifically, a locally greedy algorithm with effective backtracking ability is developed and compared to weighted A* search. The greedy algorithm shows an advantage with respect to solution cost and computation time when larger motion primitive libraries that do not operate on a regular state lattice are utilized. Linearization of the nonlinear system equations about the motion primitive library results in a hybrid linear time-varying model, and an optimal control algorithm using the L2-induced norm as the performance measure is applied to ensure that the system tracks the desired trajectory. The ability of the resulting controller to closely track the trajectory obtained from the motion planner, despite various disturbances and uncertainties, is demonstrated through simulation. Additionally, an approach for obtaining dynamically feasible reference trajectories and feedback controllers for a small unmanned aerial vehicle (UAV) based on an aerodynamic model derived from flight tests is presented. The modeling approach utilizes the two step method (TSM) with stepwise multiple regression to determine relevant explanatory terms for the aerodynamic models. Dynamically feasible trajectories are then obtained through the solution of an optimal control problem using pseudospectral optimal control software. Discrete-time feedback controllers are then obtained to regulate the vehicle along the desired reference trajectory. Simulations in a realistic operational environment as well as flight testing with the feedback controller demonstrate the capabilities of the approach. The TSM is also applied for system identification of an aircraft using motion capture data. In this application, time domain system identification techniques are used to identify both linear and nonlinear aerodynamic models of large-amplitude pitching motions driven by control surface deflections. The resulting models are assessed based on both their predictive capabilities as well as simulation results.<br>Ph. D.

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DELHAISSE, BRIAN. "Exploiting Prior Knowledge in Robot Motion Skills Learning." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/999052.

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This thesis presents a new robot learning framework, its application to exploit prior knowledge by encoding movement primitives in the form of a novel motion library, and the transfer of such knowledge to other robotic platforms in the form of shared latent spaces. In robot learning, it is often desirable to have robots that learn and acquire new skills rapidly. However, existing methods are specific to a certain task defined by the user, as well as time consuming to train. This includes for instance end-to-end models that can require a substantial amount of time to learn a certain skill. Such methods often start with no prior knowledge or little, and move slowly from erratic movements to the specific required motion. This is very different from how animals and humans learn motion skills. For instance, zebras in the African Savannah can learn to walk in few minutes just after being born. This suggests that some kind of prior knowledge is encoded into them. Leveraging this information may help improve and accelerate the learning and generation of new skills. These observations raise questions such as: how would this prior knowledge be represented? And how much would it help the learning process? Additionally, once learned, these models often do not transfer well to other robotic platforms requiring to teach to each other robot the same skills. This significantly increases the total training time and render the demonstration phase a tedious process. Would it be possible instead to exploit this prior knowledge to accelerate the learning process of new skills by transferring it to other robots? These are some of the questions that we are interested to investigate in this thesis. However, before examining these questions, a practical tool that allows one to easily test ideas in robot learning is needed. This tool would have to be easy-to-use, intuitive, generic, modular, and would need to let the user easily implement different ideas and compare different models/algorithms. Once implemented, we would then be able to focus on our original questions.

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Almehio, Yasser. "A Cumulative Framework for Image Registration using Level-line Primitives." Thesis, Paris 11, 2012. http://www.theses.fr/2012PA112155.

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Nous proposons dans cette thèse une nouvelle approche cumulative de recalage d'images basée sur des primitives construites à partir des lignes de niveaux. Les lignes de niveaux sont invariantes par rapport aux diverses perturbations affectant l'image tels que les changements de contraste. Par ailleurs, leur abondance dans une image suggère naturellement un processus de décision cumulatif. Nous proposons alors un algorithme récursif d'extraction des lignes de niveaux simple et efficace qui extrait les lignes par groupes rectiligne appelés ``segments''. Les segments sont ensuite groupés -- sous contrainte de proximité -- en fonction du modèle de transformation recherchée et afin de faciliter le calcul des invariants. Les primitives construites ont alors la forme de Z, Y ou W et sont classées en fonction de leur fiabilité, ce qui participe au paramétrage du processus de décision cumulatif. Le vote est multi-tours et constitué d'une phase préliminaire de construction de listes de préférences inspiré de la technique des mariages stables. Les primitives votent à une itération donnée en fonction de leur fiabilité. Chaque itération fournit ainsi un estimé de la transformation recherchée que le tour suivant peut raffiner. Ce procédé multi-tours permet, de ce fait, d'éliminer les ambiguïtés d'appariement générées par les motifs répétitifs présents dans les images. Notre approche a été validée pour recaler des images sous différents modèles de transformations allant de la plus simple (similarité) à la plus complexe (projective). Nous montrons dans cette thèse comment le choix pertinent de primitives basées sur les lignes de niveaux en conjonction avec un processus de décision cumulatif permet d'obtenir une méthode de recalage d'images robuste, générique et complète, fournissant alors différents niveaux de précision et pouvant ainsi s'appliquer à différents contextes<br>In this thesis, we propose a new image registration method that relies on level-line primitives. Level-lines are robust towards contrast changes and proposed primitives inherit their robustness. Moreover, their abundance in the image is well adapted to a cumulative matching process based on a multi-stage primitive election procedure. We propose a simple recursive tracking algorithm to extract level lines by straight sets called "segments". Segments are then grouped under proximity constraints to construct primitives (Z, Y and W shapes) that are classified into categories according to their reliability. Primitive shapes are defined according to the transformation model. The cumulative process is based on a preliminary step of preference lists construction that is inspired from the stable marriage matching algorithm. Primitives vote in a given voting stage according to their reliability. Each stage provides a coarse estimate of the transformation that the next stage gets to refine. This process, in turn, eliminate gradually the ambiguity happened by incorrect correspondences. Our additional contribution is to validate further geometric transformations, from simple to complex ones, completing the path "similarity, affine, projective". We show in this thesis how the choice of level lines in conjunction with a cumulative decision process allows defining a complete robust registration approach that is tested and evaluated on several real image sequences including different type of transformations

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Mangin, Olivier. "Emergence de concepts multimodaux : de la perception de mouvements primitifs à l'ancrage de mots acoustiques." Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0002/document.

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Cette thèse considère l'apprentissage de motifs récurrents dans la perception multimodale. Elle s'attache à développer des modèles robotiques de ces facultés telles qu'observées chez l'enfant, et elle s'inscrit en cela dans le domaine de la robotique développementale.Elle s'articule plus précisément autour de deux thèmes principaux qui sont d'une part la capacité d'enfants ou de robots à imiter et à comprendre le comportement d'humains, et d'autre part l'acquisition du langage. A leur intersection, nous examinons la question de la découverte par un agent en développement d'un répertoire de motifs primitifs dans son flux perceptuel. Nous spécifions ce problème et établissons son lien avec ceux de l'indétermination de la traduction décrit par Quine et de la séparation aveugle de source tels qu'étudiés en acoustique.Nous en étudions successivement quatre sous-problèmes et formulons une définition expérimentale de chacun. Des modèles d'agents résolvant ces problèmes sont également décrits et testés. Ils s'appuient particulièrement sur des techniques dites de sacs de mots, de factorisation de matrices et d'apprentissage par renforcement inverse. Nous approfondissons séparément les trois problèmes de l'apprentissage de sons élémentaires tels les phonèmes ou les mots, de mouvements basiques de danse et d'objectifs primaires composant des tâches motrices complexes. Pour finir nous étudions le problème de l'apprentissage d'éléments primitifs multimodaux, ce qui revient à résoudre simultanément plusieurs des problèmes précédents. Nous expliquons notamment en quoi cela fournit un modèle de l'ancrage de mots acoustiques<br>This thesis focuses on learning recurring patterns in multimodal perception. For that purpose it develops cognitive systems that model the mechanisms providing such capabilities to infants; a methodology that fits into thefield of developmental robotics.More precisely, this thesis revolves around two main topics that are, on the one hand the ability of infants or robots to imitate and understand human behaviors, and on the other the acquisition of language. At the crossing of these topics, we study the question of the how a developmental cognitive agent can discover a dictionary of primitive patterns from its multimodal perceptual flow. We specify this problem and formulate its links with Quine's indetermination of translation and blind source separation, as studied in acoustics.We sequentially study four sub-problems and provide an experimental formulation of each of them. We then describe and test computational models of agents solving these problems. They are particularly based on bag-of-words techniques, matrix factorization algorithms, and inverse reinforcement learning approaches. We first go in depth into the three separate problems of learning primitive sounds, such as phonemes or words, learning primitive dance motions, and learning primitive objective that compose complex tasks. Finally we study the problem of learning multimodal primitive patterns, which corresponds to solve simultaneously several of the aforementioned problems. We also details how the last problems models acoustic words grounding

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Williams, Ben H. "Extracting motion primitives from natural handwriting data." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3221.

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Humans and animals can plan and execute movements much more adaptably and reliably than current computers can calculate robotic limb trajectories. Over recent decades, it has been suggested that our brains use motor primitives as blocks to build up movements. In broad terms a primitive is a segment of pre-optimised movement allowing a simplified movement planning solution. This thesis explores a generative model of handwriting based upon the concept of motor primitives. Unlike most primitive extraction studies, the primitives here are time extended blocks that are superimposed with character specific offsets to create a pen trajectory. This thesis shows how handwriting can be represented using a simple fixed function superposition model, where the variation in the handwriting arises from timing variation in the onset of the functions. Furthermore, it is shown how handwriting style variations could be due to primitive function differences between individuals, and how the timing code could provide a style invariant representation of the handwriting. The spike timing representation of the pen movements provides an extremely compact code, which could resemble internal spiking neural representations in the brain. The model proposes an novel way to infer primitives in data, and the proposed formalised probabilistic model allows informative priors to be introduced providing a more accurate inference of primitive shape and timing.

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Mirko, Raković. "Sinteza i realizacija dvonožnog hoda putem primitiva." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2013. http://www.cris.uns.ac.rs/record.jsf?recordId=85600&source=NDLTD&language=en.

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U tezi je prikazan novi metod za sintezu i realizaciju dvonožnogveštačkog hoda koji se zasniva na upotrebi jednostavnih pokreta čijimje kombinovanjem moguće realizovati kompleksne pokrete kao što jehod, a čiji se parametri mogu menjati tokom kretanja. Time je omogućenoda se na osnovu informacija o nameravanom kretanju i stanja okolineizvrši sinteza kretanja izborom i kombinacijom jednostavnihbazičnih pokreta koje se nazivaju primitivi. Takođe je omogućeno da se,tokom izvršavanja hoda bez njegovog prekida, menjaju parametrikretanja kao što su brzina hoda, dužina koraka, pravac kretanja ivisina podizanja noge tokom prenosne faze. Potvrda je data krozeksperimentalne rezultate koji su sprovedeni simulacijom nadinamičkom modelu humanoidnog robota.<br>This dissertation presents new method for the synthesis and realization ofbiped artificial walk based on the use of simple movements that can becombined in order to achieve complex movements such as walk, whereas itis possible to change the motion parameters at any time. It means that,based on the information about intended movement and current state of theenvironment, it is possible to synthesize motion by selecting and tying simplemovements, i.e. motion primitives. It also enables the robot to changewalking parameters online such as walking speed, direction of walk, footlength during swing phase and step length. Proof of this method is given byexperimental results obtained during the simulation on a dynamic model ofhumanoid robot.

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Blanchon, Sylvain. "Etude de la diversité phénotypique et génotypique des dyskinésies ciliaires primitives : vers une prise en charge personnalisée." Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC0073.

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Rosado, José Fernando Fachada. "Adaptive biped locomotion from a single demonstration using motion primitives." Doctoral thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/17303.

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Doutoramento em Engenharia Eletrotécnica<br>Este trabalho aborda o problema de capacidade de imitação da locomoção humana através da utilização de trajetórias de baixo nível codificadas com primitivas de movimento e utilizá-las para depois generalizar para novas situações, partindo apenas de uma demonstração única. Assim, nesta linha de pensamento, os principais objetivos deste trabalho são dois: o primeiro é analisar, extrair e codificar demonstrações efetuadas por um humano, obtidas por um sistema de captura de movimento de forma a modelar tarefas de locomoção bípede. Contudo, esta transferência não está limitada à simples reprodução desses movimentos, requerendo uma evolução das capacidades para adaptação a novas situações, assim como lidar com perturbações inesperadas. Assim, o segundo objetivo é o desenvolvimento e avaliação de uma estrutura de controlo com capacidade de modelação das ações, de tal forma que a demonstração única apreendida possa ser modificada para o robô se adaptar a diversas situações, tendo em conta a sua dinâmica e o ambiente onde está inserido. A ideia por detrás desta abordagem é resolver o problema da generalização a partir de uma demonstração única, combinando para isso duas estruturas básicas. A primeira consiste num sistema gerador de padrões baseado em primitivas de movimento utilizando sistemas dinâmicos (DS). Esta abordagem de codificação de movimentos possui propriedades desejáveis que a torna ideal para geração de trajetórias, tais como a possibilidade de modificar determinados parâmetros em tempo real, tais como a amplitude ou a frequência do ciclo do movimento e robustez a pequenas perturbações. A segunda estrutura, que está embebida na anterior, é composta por um conjunto de osciladores acoplados em fase que organizam as ações de unidades funcionais de forma coordenada. Mudanças em determinadas condições, como o instante de contacto ou impactos com o solo, levam a modelos com múltiplas fases. Assim, em vez de forçar o movimento do robô a situações pré-determinadas de forma temporal, o gerador de padrões de movimento proposto explora a transição entre diferentes fases que surgem da interação do robô com o ambiente, despoletadas por eventos sensoriais. A abordagem proposta é testada numa estrutura de simulação dinâmica, sendo que várias experiências são efetuadas para avaliar os métodos e o desempenho dos mesmos.<br>This work addresses the problem of learning to imitate human locomotion actions through low-level trajectories encoded with motion primitives and generalizing them to new situations from a single demonstration. In this line of thought, the main objectives of this work are twofold: The first is to analyze, extract and encode human demonstrations taken from motion capture data in order to model biped locomotion tasks. However, transferring motion skills from humans to robots is not limited to the simple reproduction, but requires the evaluation of their ability to adapt to new situations, as well as to deal with unexpected disturbances. Therefore, the second objective is to develop and evaluate a control framework for action shaping such that the single-demonstration can be modulated to varying situations, taking into account the dynamics of the robot and its environment. The idea behind the approach is to address the problem of generalization from a single-demonstration by combining two basic structures. The first structure is a pattern generator system consisting of movement primitives learned and modelled by dynamical systems (DS). This encoding approach possesses desirable properties that make them well-suited for trajectory generation, namely the possibility to change parameters online such as the amplitude and the frequency of the limit cycle and the intrinsic robustness against small perturbations. The second structure, which is embedded in the previous one, consists of coupled phase oscillators that organize actions into functional coordinated units. The changing contact conditions plus the associated impacts with the ground lead to models with multiple phases. Instead of forcing the robot’s motion into a predefined fixed timing, the proposed pattern generator explores transition between phases that emerge from the interaction of the robot system with the environment, triggered by sensor-driven events. The proposed approach is tested in a dynamics simulation framework and several experiments are conducted to validate the methods and to assess the performance of a humanoid robot.

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Neas, Charles Bennett. "A Greedy Search Algorithm for Maneuver-Based Motion Planning of Agile Vehicles." Thesis, Virginia Tech, 2010. http://hdl.handle.net/10919/36213.

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This thesis presents a greedy search algorithm for maneuver-based motion planning of agile vehicles. In maneuver-based motion planning, vehicle maneuvers are solved offline and saved in a library to be used during motion planning. From this library, a tree of possible vehicle states can be generated through the search space. A depth-first, library-based algorithm called AD-Lib is developed and used to quickly provide feasible trajectories along the tree. AD-Lib combines greedy search techniques with hill climbing and effective backtracking to guide the search process rapidly towards the goal. Using simulations of a four-thruster hovercraft, AD-Lib is compared to existing suboptimal search algorithms in both known and unknown environments with static obstacles. AD-Lib is shown to be faster than existing techniques, at the expense of increased path cost. The motion planning strategy of AD-Lib along with a switching controller is also tested in an environment with dynamic obstacles.<br>Master of Science

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Books on the topic "Primitive motion"

1

Chow, Rey. Primitive passions: Visuality, sexuality, ethnography, and contemporary Chinese cinema. Columbia University Press, 1995.

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Jean-François, Durand, and Naumann Michel 1946-, eds. Nudité, sauvagerie, fantasmes coloniaux dans les littératures coloniales: Actes. Kailash, 2004.

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National Museum of African Art. The art of West African kingdoms. Smithsonian Institution Press (for the) National Museum of African Art, 1988.

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1945-, Lifschitz Edward, and National Museum of African Art (U.S.), eds. The Art of West African kingdoms. Published for the National Museum of African Art, by the Smithsonian Institution Press, 1987.

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Marjorie, Reeves, ed. Prophetic Rome in the High Renaissance period: Essays. Clarendon Press, 1992.

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Apichatpong Weerasethakul: Primitive. New Museum, 2011.

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Oksiloff, A. Picturing the Primitive: Visual Culture, Ethnography, and Early German Cinema. Palgrave Macmillan, 2016.

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Nail, Thomas. Marx in Motion. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780197526477.001.0001.

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Socialism is back, and with it comes a renewed interest in Marx’s critique of capitalism. After the 2008 financial crash, international book sales of Capital exploded for the first time in decades. In a world of rising income inequality, right-wing nationalisms, and global climate change, people are looking to the father of modern socialism for answers. This book has been written to help those returning to Marx get answers to their pressing questions about the nature of wealth, ecological crisis, gender inequality, colonialism, migration, and the possibility of socialism. This book also offers readers a new perspective on several major ideas in Marx’s work. It argues that Marx, contrary to conventional wisdom, did not think history was deterministic or that reality could be reduced to classical materialism. Marx was not an anthropocentric humanist, nor did he have a labor theory of value. The unique contribution of this book is that it begins with Marx’s earliest and most neglected book on ancient naturalism in order to show its lasting methodological effect on his “process materialism,” defined by the primacy of motion. This “kinetic Marxism” offers a new way to reread Capital that bears directly on a number of contemporary issues. This also makes Marx in Motion the first book to offer a new materialist reading of Marx. The result is a fresh new view on the important theories of primitive accumulation, metabolism, value, fetishism, dialectics, and the possibility of a kinetic communism for the twenty-first century.

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Zeitlin, Vladimir. Simplifying Primitive Equations: Rotating Shallow-Water Models and their Properties. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198804338.003.0003.

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In this chapter, one- and two-layer versions of the rotating shallow-water model on the tangent plane to the rotating, and on the whole rotating sphere, are derived from primitive equations by vertical averaging and columnar motion (mean-field) hypothesis. Main properties of the models including conservation laws and wave-vortex dichotomy are established. Potential vorticity conservation is derived, and the properties of inertia–gravity waves are exhibited. The model is then reformulated in Lagrangian coordinates, variational principles for its one- and two-layer version are established, and conservation laws are reinterpreted in these terms.

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Zeitlin, Vladimir. Primitive Equations Model. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198804338.003.0002.

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The chapter gives the foundations of modelling of large-scale atmospheric and oceanic motions and presents the ‘primitive equations’ (PE) model. After a concise reminder on general fluid mechanics, the main hypotheses leading to the PE model are explained, together with the tangent-plane (so-called f and beta plane) approximations, and ‘traditional’ approximation to the hydrodynamical equations on the rotating sphere. PE are derived in parallel for the ocean and for the atmosphere. It is then shown that, with a judicious choice of the vertical coordinate, the ‘pseudo-height’, in the atmosphere, these two sets of equations are practically equivalent. The main properties of PE are derived and the key concepts of wave–vortex dichotomy, and of slow and fast motions, are explained. The essential notion of potential vorticity is introduced and its conservation by fluid masses is demonstrated. Inertia–gravity waves are explained and their properties presented. Limitations of the hydrostatic hypothesis are demonstrated.

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Book chapters on the topic "Primitive motion"

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Belkind, Ori. "Primitive Motion Relationalism." In Physical Systems. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2373-3_3.

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Osaki, Ryuta, Mitsuomi Shimada, and Kuniaki Uehara. "Extraction of Primitive Motion for Human Motion Recognition." In Discovery Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-46846-3_44.

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Osaki, Ryuta, Mitsuomi Shimada, and Kuniaki Uehara. "A Motion Recognition Method by Using Primitive Motions." In Advances in Visual Information Management. Springer US, 2000. http://dx.doi.org/10.1007/978-0-387-35504-7_8.

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Lee, Chan-Su, and Ahmed Elgammal. "Human Motion Synthesis by Motion Manifold Learning and Motion Primitive Segmentation." In Articulated Motion and Deformable Objects. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11789239_48.

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Endres, Dominik, Enrico Chiovetto, and Martin A. Giese. "Bayesian Approaches for Learning of Primitive-Based Compact Representations of Complex Human Activities." In Dance Notations and Robot Motion. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25739-6_6.

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Uehara, Kuniaki, and Mitsuomi Shimada. "Extraction of Primitive Motion and Discovery of Association Rules from Human Motion Data." In Progress in Discovery Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45884-0_24.

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Zhao, Xinyi, Hang Du, and Hanchen Lu. "Collision-Free Motion-Primitive-Based Motion Planning Algorithm for Fixed-Wing Robotic Aircraft." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6613-2_575.

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Ghosh, Mukulika, Shawna Thomas, and Nancy M. Amato. "Fast Collision Detection for Motion Planning Using Shape Primitive Skeletons." In Springer Proceedings in Advanced Robotics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44051-0_3.

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Diep, Nguyen Ngoc, Cuong Pham, and Tu Minh Phuong. "Motion Primitive Forests for Human Activity Recognition Using Wearable Sensors." In PRICAI 2016: Trends in Artificial Intelligence. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42911-3_29.

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Ando, Taisei, Takehiko Yamaguchi, Norito Kohama, Maiko Sakamoto, Tania Giovannetti, and Tetsuya Harada. "Motion Primitive Segmentation Based on Cognitive Model in VR-IADL." In HCI International 2021 - Late Breaking Papers: Multimodality, eXtended Reality, and Artificial Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90963-5_17.

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Conference papers on the topic "Primitive motion"

1

Feit, Andrew, and Berenice Mettler. "Information-Based Analysis of Visual Cues in Human Guidance." In Vertical Flight Society 72nd Annual Forum & Technology Display. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11578.

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Perception plays a central role in such humans' motion guidance skills as precision rotorcraft landing or driving a car. This paper investigates the information available in visible cues, relative to a first-person motion guidance task, in terms of sensory-motor guidance primitives. Human subjects performed a motion guidance task in a 3D simulation system, using only visual information. Guidance primitive patterns were identified from recorded subject motion and perception behavior. Information transfer between visual measurements and vehicle motion is quantified from the resulting data to identify both model-based and nonrepresentational guidance strategies. Results show that subjects use separate guidance primitive modes during distinct phases of a trajectory. This understanding of perceptual and guidance primitive elements in human behavior has applications ranging from improved augmented cuing for human pilots to advancing artificial perception in autonomous vehicle systems.

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Ubellacker, Wyatt, and Aaron D. Ames. "Learned Regions of Attraction for Safe Motion Primitive Transitions." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801671.

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Benton, Andrew, Eugen Solowjow, and Prithvi Akella. "Verifiable Learned Behaviors via Motion Primitive Composition: Applications to Scooping of Granular Media." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611279.

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Hu, Jiangpeng, Fan Yang, Fang Nan, and Marco Hutter. "Motion Primitives Planning For Center-Articulated Vehicles." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801937.

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Zhao, Zhixin, Antoine Girard, and Sorin Olaru. "Symbolically Synthesized Motion Primitives for Autonomous Navigation." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886590.

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Enes, Aaron R., and Wayne J. Book. "Recursive algorithm for motion primitive estimation." In 2011 IEEE International Conference on Robotics and Automation (ICRA 2011). IEEE, 2011. http://dx.doi.org/10.1109/icra.2011.5980456.

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Saetti, Umberto, and Jonathan Rogers. "A Motion Primitive Perspective on Rotorcraft Regime Recognition." In Vertical Flight Society 76th Annual Forum & Technology Display. The Vertical Flight Society, 2020. http://dx.doi.org/10.4050/f-0076-2020-16266.

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An alternative approach to regime recognition that is based on the notion of motion primitives is developed. The algorithm developed is non-causal and leverages the ideas of maneuvers and trims as defined in a motion primitive context. The algorithm functions in three major steps. Given a state and control input time history obtained from flight data, the first step consists of classifying the state and control time history into trim and maneuver segments. The second step leverages the information in the trim state and control vectors to classify each trim segment into a particular trim condition based on conditional (if-else-if) logic. The third step entails the classification of each maneuver segment (flown between two trim segments) as a particular maneuver condition. Importantly, maneuver classification leverages dynamic time warping in order to compensate for rate and time duration variations. Accuracy of the proposed algorithm is evaluated using SH-60B simulated flight data. Operation of the algorithm is also demonstrated using real-world piloted flight test data from a generic utility helicopter.

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Stulp, F., E. Theodorou, M. Kalakrishnan, P. Pastor, L. Righetti, and S. Schaal. "Learning motion primitive goals for robust manipulation." In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). IEEE, 2011. http://dx.doi.org/10.1109/iros.2011.6048517.

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Stulp, Freek, Evangelos Theodorou, Mrinal Kalakrishnan, Peter Pastor, Ludovic Righetti, and Stefan Schaal. "Learning motion primitive goals for robust manipulation." In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). IEEE, 2011. http://dx.doi.org/10.1109/iros.2011.6094877.

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Kulic, D., Dongheui Lee, and Y. Nakamura. "Whole body motion primitive segmentation from monocular video." In 2009 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2009. http://dx.doi.org/10.1109/robot.2009.5152266.

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