Journal articles: '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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Nemec, Bojan, and Aleš Ude. "Action sequencing using dynamic movement primitives." Robotica 30, no. 5 (2011): 837–46. http://dx.doi.org/10.1017/s0263574711001056.

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SUMMARYGeneral-purpose autonomous robots must have the ability to combine the available sensorimotor knowledge in order to solve more complex tasks. Such knowledge is often given in the form of movement primitives. In this paper, we investigate the problem of sequencing of movement primitives. We selected nonlinear dynamic systems as the underlying sensorimotor representation because they provide a powerful machinery for the specification of primitive movements. We propose two new methodologies which both ensure that consecutive movement primitives are joined together in a continuous way (up to second-order derivatives). The first is based on proper initialization of the third-order dynamic motion primitives and the second uses online Gaussian kernel functions modification of the second-order dynamic motion primitives. Both methodologies were validated by simulation and by experimentally using a Mitsubishi PA-10 articulated robot arm. Experiments comprehend pouring, table wiping, and carrying a glass of liquid.

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Zhang, Yue, Jian’an Zong, Xianzhong Gao, and Zhongxi Hou. "An Efficient Trajectory Planning Method for High-Speed Interception of Invasive Drones." Applied Sciences 14, no. 16 (2024): 7030. http://dx.doi.org/10.3390/app14167030.

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This article introduces a rapid interception trajectory generation algorithm tailored for the mitigation of malicious drone activities and other high-speed airborne threats. The proposed method facilitates a high degree of flexibility in defining the terminal state parameters, including position, velocity, and acceleration, as well as the anticipated duration of drone maneuvers, thereby enabling the fulfillment of a variety of mission objectives. The approach employed in this study linearizes the aerodynamic resistance model and computes an efficient closed-form solution for the optimal trajectory motion primitive by applying Pontryagin’s Maximum Principle. Concurrently, it minimizes the cost function associated with the aggression of control inputs. The motion primitive is defined by the combination of the initial and terminal states of the drone, as well as the expected movement time. An efficient input feasibility verification method has been designed for the optimal trajectory. This algorithm can serve as a low-level trajectory generator for advanced task planning methods. After compilation, it can evaluate and compare thousands of motion primitives per second on a personal portable computer, thereby achieving certain advanced goals. The reliability of the algorithm is verified by setting up a multi-objective approach task in a physical simulation environment.

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Salamat, Nadeem, and El-hadi Zahzah. "Spatiotemporal Relations and Modeling Motion Classes by Combined Topological and Directional Relations Method." ISRN Machine Vision 2012 (May 16, 2012): 1–11. http://dx.doi.org/10.5402/2012/872687.

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Defining spatiotemporal relations and modeling motion events are emerging issues of current research. Motion events are the subclasses of spatiotemporal relations, where stable and unstable spatio-temporal topological relations and temporal order of occurrence of a primitive event play an important role. In this paper, we proposed a theory of spatio-temporal relations based on topological and orientation perspective. This theory characterized the spatiotemporal relations into different classes according to the application domain and topological stability. This proposes a common sense reasoning and modeling motion events in diverse application with the motion classes as primitives, which describe change in orientation and topological relations model. Orientation information is added to remove the locative symmetry of topological relations from motion events, and these events are defined as a systematic way. This will help to improve the understanding of spatial scenario in spatiotemporal applications.

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Lee, Chung-Ching, and Jacques M. Hervé. "Type synthesis of primitive Schoenflies-motion generators." Mechanism and Machine Theory 44, no. 10 (2009): 1980–97. http://dx.doi.org/10.1016/j.mechmachtheory.2009.06.001.

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Zhong, Yun, and Yiannis Demiris. "DanceMVP: Self-Supervised Learning for Multi-Task Primitive-Based Dance Performance Assessment via Transformer Text Prompting." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (2024): 10270–78. http://dx.doi.org/10.1609/aaai.v38i9.28893.

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Dance is generally considered to be complex for most people as it requires coordination of numerous body motions and accurate responses to the musical content and rhythm. Studies on automatic dance performance assessment could help people improve their sensorimotor skills and promote research in many fields, including human motion analysis and motion generation. Recent papers on dance performance assessment usually evaluate simple dance motions with a single task - estimating final performance scores. In this paper, we propose DanceMVP: multi-task dance performance assessment via text prompting that solves three related tasks - (i) dance vocabulary recognition, (ii) dance performance scoring and (iii) dance rhythm evaluation. In the pre-training phase, we contrastively learn the primitive-based features of complex dance motion and music using the InfoNCE loss. For the downstream task, we propose a transformer-based text prompter to perform multi-task evaluations for the three proposed assessment tasks. Also, we build a multimodal dance-music dataset named ImperialDance. The novelty of our ImperialDance is that it contains dance motions for diverse expertise levels and a significant amount of repeating dance sequences for the same choreography to keep track of the dance performance progression. Qualitative results show that our pre-trained feature representation could cluster dance pieces for different dance genres, choreographies, expertise levels and primitives, which generalizes well on both ours and other dance-music datasets. The downstream experiments demonstrate the robustness and improvement of our method over several ablations and baselines across all three tasks, as well as monitoring the users' dance level progression.

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Prados, Adrian, Gonzalo Espinoza, Luis Moreno, and Ramon Barber. "Segment, Compare, and Learn: Creating Movement Libraries of Complex Task for Learning from Demonstration." Biomimetics 10, no. 1 (2025): 64. https://doi.org/10.3390/biomimetics10010064.

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Motion primitives are a highly useful and widely employed tool in the field of Learning from Demonstration (LfD). However, obtaining a large number of motion primitives can be a tedious process, as they typically need to be generated individually for each task to be learned. To address this challenge, this work presents an algorithm for acquiring robotic skills through automatic and unsupervised segmentation. The algorithm divides tasks into simpler subtasks and generates motion primitive libraries that group common subtasks for use in subsequent learning processes. Our algorithm is based on an initial segmentation step using a heuristic method, followed by probabilistic clustering with Gaussian Mixture Models. Once the segments are obtained, they are grouped using Gaussian Optimal Transport on the Gaussian Processes (GPs) of each segment group, comparing their similarities through the energy cost of transforming one GP into another. This process requires no prior knowledge, it is entirely autonomous, and supports multimodal information. The algorithm enables generating trajectories suitable for robotic tasks, establishing simple primitives that encapsulate the structure of the movements to be performed. Its effectiveness has been validated in manipulation tasks with a real robot, as well as through comparisons with state-of-the-art algorithms.

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Lee, Chung-Ching, and Jacques M. Hervé. "On some applications of primitive Schönflies-motion generators." Mechanism and Machine Theory 44, no. 12 (2009): 2153–63. http://dx.doi.org/10.1016/j.mechmachtheory.2009.06.005.

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Kussaba, Hugo T. M., Abdalla Swikir, Fan Wu, Anastasija Demerdjieva, Gitta Kutyniok, and Sami Haddadin. "Learning optimal controllers: a dynamical motion primitive approach." IFAC-PapersOnLine 56, no. 2 (2023): 4776–82. http://dx.doi.org/10.1016/j.ifacol.2023.10.1242.

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JENKINS, ODEST CHADWICKE, GERMÁN GONZÁLEZ SERRANO, and MATTHEW M. LOPER. "INTERACTIVE HUMAN POSE AND ACTION RECOGNITION USING DYNAMICAL MOTION PRIMITIVES." International Journal of Humanoid Robotics 04, no. 02 (2007): 365–85. http://dx.doi.org/10.1142/s0219843607001060.

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There is currently a division between real-world human performance and the decision making of socially interactive robots. This circumstance is partially due to the difficulty in estimating human cues, such as pose and gesture, from robot sensing. Towards bridging this division, we present a method for kinematic pose estimation and action recognition from monocular robot vision through the use of dynamical human motion vocabularies. Our notion of a motion vocabulary is comprised of movement primitives that structure a human's action space for decision making and predict human movement dynamics. Through prediction, such primitives can be used to both generate motor commands for specific actions and perceive humans performing those actions. In this paper, we focus specifically on the perception of human pose and performed actions using a known vocabulary of primitives. Given image observations over time, each primitive infers pose independently using its expected dynamics in the context of a particle filter. Pose estimates from a set of primitives inferencing in parallel are arbitrated to estimate the action being performed. The efficacy of our approach is demonstrated through interactive-time pose and action recognition over extended motion trials. Results evidence our approach requires small numbers of particles for tracking, is robust to unsegmented multi-action movement, movement speed, camera viewpoint and is able to recover from occlusions.

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Jia, Lesong, Xiaozhou Zhou, Hao Qin, Ruidong Bai, Liuqing Wang, and Chengqi Xue. "Research on Discrete Semantics in Continuous Hand Joint Movement Based on Perception and Expression." Sensors 21, no. 11 (2021): 3735. http://dx.doi.org/10.3390/s21113735.

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Continuous movements of the hand contain discrete expressions of meaning, forming a variety of semantic gestures. For example, it is generally considered that the bending of the finger includes three semantic states of bending, half bending, and straightening. However, there is still no research on the number of semantic states that can be conveyed by each movement primitive of the hand, especially the interval of each semantic state and the representative movement angle. To clarify these issues, we conducted experiments of perception and expression. Experiments 1 and 2 focused on perceivable semantic levels and boundaries of different motion primitive units from the perspective of visual semantic perception. Experiment 3 verified and optimized the segmentation results obtained above and further determined the typical motion values of each semantic state. Furthermore, in Experiment 4, the empirical application of the above semantic state segmentation was illustrated by using Leap Motion as an example. We ended up with the discrete gesture semantic expression space both in the real world and Leap Motion Digital World, containing the clearly defined number of semantic states of each hand motion primitive unit and boundaries and typical motion angle values of each state. Construction of this quantitative semantic expression will play a role in guiding and advancing research in the fields of gesture coding, gesture recognition, and gesture design.

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Gong, Shiqiu, Jing Zhao, Ziqiang Zhang, and Biyun Xie. "Task motion planning for anthropomorphic arms based on human arm movement primitives." Industrial Robot: the international journal of robotics research and application 47, no. 5 (2020): 669–81. http://dx.doi.org/10.1108/ir-12-2019-0261.

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Purpose This paper aims to introduce the human arm movement primitive (HAMP) to express and plan the motions of anthropomorphic arms. The task planning method is established for the minimum task cost and a novel human-like motion planning method based on the HAMPs is proposed to help humans better understand and plan the motions of anthropomorphic arms. Design/methodology/approach The HAMPs are extracted based on the structure and motion expression of the human arm. A method to slice the complex tasks into simple subtasks and sort subtasks is proposed. Then, a novel human-like motion planning method is built through the selection, sequencing and quantification of HAMPs. Finally, the HAMPs are mapped to the traditional joint angles of a robot by an analytical inverse kinematics method to control the anthropomorphic arms. Findings For the exploration of the motion laws of the human arm, the human arm motion capture experiments on 12 subjects are performed. The results show that the motion laws of human arm are reflected in the selection, sequencing and quantification of HAMPs. These motion laws can facilitate the human-like motion planning of anthropomorphic arms. Originality/value This study presents the HAMPs and a method for selecting, sequencing and quantifying them in human-like style, which leads to a new motion planning method for the anthropomorphic arms. A similar methodology is suitable for robots with anthropomorphic arms such as service robots, upper extremity exoskeleton robots and humanoid robots.

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Wei, Yuan. "An Intelligent Human-like Motion Planner for Anthropomorphic Arms Based on Diversified Arm Motion Models." Electronics 12, no. 6 (2023): 1316. http://dx.doi.org/10.3390/electronics12061316.

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In this paper, the human-like motion issue for anthropomorphic arms is further discussed. An Intelligent Human-like Motion Planner (IHMP) consisting of Movement Primitive (MP), Bayesian Network (BN) and Coupling Neural Network (CPNN) is proposed to help the robot generate human-like arm movements. Firstly, the arm motion model is decoupled in the aspects of arm structure and motion process, respectively. In the former aspect, the arm model is decoupled into different simple models through the Movement Primitive. A Hierarchical Planning Strategy (HPS) is proposed to decouple a complete motion process into different sub-processes. Based on diversified arm motion models, the Bayesian Network is used to help the robot choose the suitable motion model among these arm motion models. Then, according to the features of diversified arm motion models, the Coupling Neural Network is proposed to obtain the inverse kinematic (IK) solutions. This network can integrate different models into a single network and reflect the features of these models by changing the network structure. Being a major contribution to this paper, specific focus is on the improvement of human-like motion accuracy and independent consciousness of robots. Finally, the availability of the IHMP is verified by experiments on a humanoid robot Pepper.

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Mueller, Mark W., Markus Hehn, and Raffaello D'Andrea. "A Computationally Efficient Motion Primitive for Quadrocopter Trajectory Generation." IEEE Transactions on Robotics 31, no. 6 (2015): 1294–310. http://dx.doi.org/10.1109/tro.2015.2479878.

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Gutow, Geordan, and Jonathan D. Rogers. "Koopman Operator Method for Chance-Constrained Motion Primitive Planning." IEEE Robotics and Automation Letters 5, no. 2 (2020): 1572–78. http://dx.doi.org/10.1109/lra.2020.2969187.

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TAKANO, Ken, Tetsushi NAKAI, and Ken SASAKI. "Control Level Analysis of Primitive Motion of Handling RectangularSolid." Journal of the Japan Society for Precision Engineering 65, no. 7 (1999): 1051–55. http://dx.doi.org/10.2493/jjspe.65.1051.

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Corona-Castuera, J., and I. Lopez-Juarez. "Behaviour-based approach for skill acquisition during assembly operations, starting from scratch." Robotica 24, no. 6 (2006): 657–71. http://dx.doi.org/10.1017/s0263574706002839.

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Industrial robots in poorly structured environments have to interact compliantly with this environment for successful operations. In this paper, we present a behaviour-based approach to learn peg-in-hole operations from scratch. The robot learns autonomously the initial mapping between contact states to motion commands employing fuzzy rules and creating an Acquired-Primitive Knowledge Base (ACQ-PKB), which is later used and refined on-line by a Fuzzy ARTMAP neural network-based controller. The effectiveness of the approach is tested comparing the compliant motion behaviour using the ACQ-PKB and a priori Given-Primitive Knowledge Base (GVN-PKB). Results using a KUKA KR15 industrial robot validate the approach.

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Suomalainen, Markku, Fares J. Abu-dakka, and Ville Kyrki. "Imitation learning-based framework for learning 6-D linear compliant motions." Autonomous Robots 45, no. 3 (2021): 389–405. http://dx.doi.org/10.1007/s10514-021-09971-y.

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AbstractWe present a novel method for learning from demonstration 6-D tasks that can be modeled as a sequence of linear motions and compliances. The focus of this paper is the learning of a single linear primitive, many of which can be sequenced to perform more complex tasks. The presented method learns from demonstrations how to take advantage of mechanical gradients in in-contact tasks, such as assembly, both for translations and rotations, without any prior information. The method assumes there exists a desired linear direction in 6-D which, if followed by the manipulator, leads the robot’s end-effector to the goal area shown in the demonstration, either in free space or by leveraging contact through compliance. First, demonstrations are gathered where the teacher explicitly shows the robot how the mechanical gradients can be used as guidance towards the goal. From the demonstrations, a set of directions is computed which would result in the observed motion at each timestep during a demonstration of a single primitive. By observing which direction is included in all these sets, we find a single desired direction which can reproduce the demonstrated motion. Finding the number of compliant axes and their directions in both rotation and translation is based on the assumption that in the presence of a desired direction of motion, all other observed motion is caused by the contact force of the environment, signalling the need for compliance. We evaluate the method on a KUKA LWR4+ robot with test setups imitating typical tasks where a human would use compliance to cope with positional uncertainty. Results show that the method can successfully learn and reproduce compliant motions by taking advantage of the geometry of the task, therefore reducing the need for localization accuracy.

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Dallal Bashi, Omar I., Husamuldeen K. Hameed, Yasir Mahmood Al Kubaisi, and Ahmad H. Sabry. "Developing a model for unmanned aerial vehicle with fixed-wing using 3D-map exploring rapidly random tree technique." Bulletin of Electrical Engineering and Informatics 13, no. 1 (2024): 473–81. http://dx.doi.org/10.11591/eei.v13i1.5305.

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While the motion planning algorithms consider the obstacles that were known in the map, it is possible to use obstacle avoidance algorithms to take over and send commands to theunmanned aerial vehicle (UAV), when there is an unknown obstacle on the way. The rapidly random tree (RRT) algorithm is used to plan paths for a quad-copter or a fixed-wing UAV. This work develops a model for UAV with fixed-wing using a 3D map exploring the RRT technique. The first step is to obtain a 3D occupancy map from the map data stored in the UAV city to provide a map with some pre-generated obstacles. The contribution of this work is to use RRT planning for 3D state space, where the motion segment or motion primitive connecting the two consecutive states should be defined in a 3D space while satisfying the motion constraints of a UAV. The simulation includes setting up a 3D map, providing the starting and destination pose, planning a way using RRT and 3D Dubins moving primitives, smoothing the acquired trajectory, and simulating the UAV flight. The results obtained demonstrate that the smoothed-generated waypoints significantly improved tracking in general with shorter paths.

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Bevilacqua, Antonio, Giovanni Ciampi, Rob Argent, Brian Caulfield, and Tahar Kechadi. "Combining Real-Time Segmentation and Classification of Rehabilitation Exercises with LSTM Networks and Pointwise Boosting." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 08 (2020): 13229–34. http://dx.doi.org/10.1609/aaai.v34i08.7028.

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Autonomous biofeedback tools in support of rehabilitation patients are commonly built as multi-tier pipelines, where a segmentation algorithm is first responsible for isolating motion primitives, and then classification can be performed on each primitive. In this paper, we present a novel segmentation technique that integrates on-the-fly qualitative classification of physical movements in the process. We adopt Long Short-Term Memory (LSTM) networks to model the temporal patterns of a streaming multivariate time series, obtained by sampling acceleration and angular velocity of the limb in motion, and then we aggregate the pointwise predictions of each isolated movement using different boosting methods. We tested our technique against a dataset composed of four common lower-limb rehabilitation exercises, collected from heterogeneous populations (clinical and healthy). Experimental results are promising and show that combining segmentation and classification of orthopaedic movements is a valid method with many potential real-world applications.

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MATHUR, MUKUT B. "Numerical prediction of tropical cyclone motion." MAUSAM 41, no. 2 (2022): 180–83. http://dx.doi.org/10.54302/mausam.v41i2.2612.

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The motion of hurricanes is investigated with a fine-mesh quasi-Lagrangian primitive equation model. The model is integrated to 72 hours using symmetric vortices of different size and intensity, and a uniform steering current. The vortex is found to intensity to hurricane strength in each case, and large asymmetries in the wind field develop.. The structure of the asymmetries is discussed. It IS suggested that the motion of the storms is related to the structure of these asymmetries.

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Cho, YoonAh, JooHyu Park, and Soon-Bum Lim. "Development of Primitive Motion Library for Kinetic Typography Rendering Engine." International Journal of Smart Home 9, no. 3 (2015): 13–22. http://dx.doi.org/10.14257/ijsh.2015.9.3.02.

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Choi, Kevin S., Zachary C. Goddard, Samuel J. Deal, Anirban Mazumdar, and Kyle Williams. "Leveraging Machine Learning to Improve Adaptive Primitive-Based Motion Planning." Journal of Aerospace Information Systems 21, no. 9 (2024): 751–60. http://dx.doi.org/10.2514/1.i011285.

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This paper introduces a new approach for adding replanning capabilities to the maneuver automaton. We call this approach “maneuver interruption.” Maneuver interruption enables replanning by identifying maneuver segments that are dynamically similar to the current vehicle state. As a result, the vehicle can exit a maneuver if new information emerges or the environment changes. We use machine learning to enhance the performance of maneuver interruption. Specifically, we examine how supervised learning can predict dynamic similarity and utilize the learned network to enable maneuver interruption. A variety of models are compared for their ability to quantify the feasibility of a maneuver-to-maneuver transition. The multilayer perceptron is found to be the most effective at this task and was therefore selected for generating maneuver-to-maneuver transitions for replanning. Additionally, we use Monte Carlo methods and pruning to reduce the transition library size by an order of magnitude with minimal loss in performance. We test learning-enhanced maneuver interruption on obstacle evasion tasks with a medium-fidelity ZOHD Drift flight dynamics model. On randomly generated obstacle fields, maneuver interruption is demonstrated to enable longer collision-free flights at a minor cost to control performance.

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Tort, Marine, Thomas Dubos, François Bouchut, and Vladimir Zeitlin. "Consistent shallow-water equations on the rotating sphere with complete Coriolis force and topography." Journal of Fluid Mechanics 748 (May 8, 2014): 789–821. http://dx.doi.org/10.1017/jfm.2014.172.

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AbstractConsistent shallow-water equations are derived on the rotating sphere with topography retaining the Coriolis force due to the horizontal component of the planetary angular velocity. Unlike the traditional approximation, this ‘non-traditional’ approximation captures the increase with height of the solid-body velocity due to planetary rotation. The conservation of energy, angular momentum and potential vorticity are ensured in the system. The caveats in extending the standard shallow-water wisdom to the case of the rotating sphere are exposed. Different derivations of the model are possible, being based, respectively, on (i) Hamilton’s principle for primitive equations with a complete Coriolis force, under the hypothesis of columnar motion, (ii) straightforward vertical averaging of the ‘non-traditional’ primitive equations, and (iii) a time-dependent change of independent variables in the primitive equations written in the curl (‘vector-invariant’) form, with subsequent application of the columnar motion hypothesis. An intrinsic, coordinate-independent form of the non-traditional equations on the sphere is then given, and used to derive hyperbolicity criteria and Rankine–Hugoniot conditions for weak solutions. The relevance of the model for the Earth’s atmosphere and oceans, as well as other planets, is discussed.

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Toner, Tyler, Dawn M. Tilbury, and Kira Barton. "PRF: A Program Reuse Framework for Automated Programming by Learning from Existing Robot Programs." Robotics 13, no. 8 (2024): 118. http://dx.doi.org/10.3390/robotics13080118.

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This paper explores the problem of automated robot program generation from limited historical data when neither accurate geometric environmental models nor online vision feedback are available. The Program Reuse Framework (PRF) is developed, which uses expert-defined motion classes, a novel data structure introduced in this work, to learn affordances, workspaces, and skills from historical data. Historical data comprise raw robot joint trajectories and descriptions of the robot task being completed. Given new tasks, motion classes are then used again to formulate an optimization problem capable of generating new open-loop, skill-based programs to complete the tasks. To cope with a lack of geometric models, a technique to learn safe workspaces from demonstrations is developed, allowing the risk of new programs to be estimated before execution. A new learnable motion primitive for redundant manipulators is introduced, called a redundancy dynamical movement primitive, which enables new end-effector goals to be reached while mimicking the whole-arm behavior of a demonstration. A mobile manipulator part transportation task is used throughout to illustrate each step of the framework.

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Cui, Ze, Lang Kou, Zenghao Chen, et al. "Research on LFD System of Humanoid Dual-Arm Robot." Symmetry 16, no. 4 (2024): 396. http://dx.doi.org/10.3390/sym16040396.

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Although robots have been widely used in a variety of fields, the idea of enabling them to perform multiple tasks in the same way that humans do remains a difficulty. To solve this, we investigate the learning from demonstration (LFD) system with our independently designed symmetrical humanoid dual-arm robot. We present a novel action feature matching algorithm. This algorithm accurately transforms human demonstration data into task models that robots can directly execute, considerably improving LFD’s generalization capabilities. In our studies, we used motion capture cameras to capture human demonstration actions, which included combinations of simple actions (the action layer) and a succession of complicated operational tasks (the task layer). For the action layer data, we employed Gaussian mixture models (GMM) for processing and constructing an action primitive library. As for the task layer data, we created a “keyframe” segmentation method to transform this data into a series of action primitives and build another action primitive library. Guided by our algorithm, the robot successfully imitated complex human tasks. Results show its excellent task learning and execution, providing an effective solution for robots to learn from human demonstrations and significantly advancing robot technology.

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Kingston, Zachary, Mark Moll, and Lydia E. Kavraki. "Sampling-Based Methods for Motion Planning with Constraints." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (2018): 159–85. http://dx.doi.org/10.1146/annurev-control-060117-105226.

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Robots with many degrees of freedom (e.g., humanoid robots and mobile manipulators) have increasingly been employed to accomplish realistic tasks in domains such as disaster relief, spacecraft logistics, and home caretaking. Finding feasible motions for these robots autonomously is essential for their operation. Sampling-based motion planning algorithms are effective for these high-dimensional systems; however, incorporating task constraints (e.g., keeping a cup level or writing on a board) into the planning process introduces significant challenges. This survey describes the families of methods for sampling-based planning with constraints and places them on a spectrum delineated by their complexity. Constrained sampling-based methods are based on two core primitive operations: ( a) sampling constraint-satisfying configurations and ( b) generating constraint-satisfying continuous motion. Although this article presents the basics of sampling-based planning for contextual background, it focuses on the representation of constraints and sampling-based planners that incorporate constraints.

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Catanese, A., F. Malacario, L. Cirillo, et al. "Application of intravoxel incoherent motion (IVIM) magnetic resonance imaging in the evaluation of primitive brain tumours." Neuroradiology Journal 31, no. 1 (2017): 4–9. http://dx.doi.org/10.1177/1971400917693025.

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Intravoxel incoherent motion is a potential non-invasive diagnostic tool in brain tumours, without any clear guidelines for its evaluation yet. In our study, we compare intravoxel incoherent motion with dynamic susceptibility contrast magnetic resonance imaging in the quantification of tumour tissue blood perfusion in 28 patients affected by brain tumours, highlighting the issues encountered during the acquisition set-up and post-processing steps. Intravoxel incoherent motion is a new imaging tool and an alternative technique to dynamic susceptibility contrast-magnetic resonance imaging which is of considerable interest at present. This is partly because it does not require the use of a contrast agent and relies on the intrinsic properties of motion in the capillaries of the spins. Compared to dynamic susceptibility contrast-magnetic resonance imaging, the intravoxel incoherent motion technique is also characterised by better resolution because the gadolinium-based contrast agent bolus used in the standard technique results in a variation by more than 50% of the signal coming from the brain. Finally, intravoxel incoherent motion is more sensitive to the incoherent motion that originates from small capillary vessels, while the dynamic susceptibility contrast signal is also contaminated by the input from larger arteries and veins, which may result in an overestimation of the blood volume. Although there are limitations due to the heterogeneity of the sample considered in our study, intravoxel incoherent motion has been shown to be an accurate noninvasive radiological biomarker, useful to distinguish between low and high grade glial tumours.

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Wang, Yifan, Hanxu Sun, Gang Chen, Qingxuan Jia, and Boyang Yu. "Hierarchical Task Planning for Multiarm Robot with Multiconstraint." Mathematical Problems in Engineering 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/2508304.

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Multiarm systems become the trends of space robots, for the on-orbit servicing missions are becoming more complex and various. A hierarchical task planning method with multiconstraint for multiarm space robot is presented in this paper. The process of task planning is separated into two hierarchies: mission profile analysis and task node planning. In mission profile analysis, several kinds of primitive tasks and operators are defined. Then, a complex task can be decomposed into a sequence of primitive tasks by using hierarchical task network (HTN) with those primitive tasks and operators. In task node planning,A⁎algorithm is improved to adapt the continuous motion of manipulator. Then, some of the primitive tasks which cannot be executed directly because of constraints are further decomposed into several task nodes by using improvedA⁎algorithm. Finally, manipulators execute the task by moving from one node to another with a simple path plan algorithm. The feasibility and effectiveness of the proposed task planning method are verified by simulation.

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Ohta, N., Y. Shinguu, K. Nagatani, and Y. Tanaka. "1A1-B6 Realization of "Returning Books Motion" by Mobile Manibulator : Design of Primitive Motions and Integration." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2001 (2001): 7. http://dx.doi.org/10.1299/jsmermd.2001.7_2.

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Gutow, Geordan, and Jonathan D. Rogers. "AND/OR search techniques for chance constrained motion primitive path planning." Robotics and Autonomous Systems 149 (March 2022): 103991. http://dx.doi.org/10.1016/j.robot.2021.103991.

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Lin, Jonathan Feng-Shun, Michelle Karg, and Dana Kulic. "Movement Primitive Segmentation for Human Motion Modeling: A Framework for Analysis." IEEE Transactions on Human-Machine Systems 46, no. 3 (2016): 325–39. http://dx.doi.org/10.1109/thms.2015.2493536.

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Cho, YoonAh, Sung-Ho Woo, and Soon-Bum Lim. "Design and Implementation of the Primitive Motion API for Kinetic Typography." Journal of Korea Multimedia Society 18, no. 6 (2015): 763–71. http://dx.doi.org/10.9717/kmms.2015.18.6.763.

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Shkolnik, Alexander, Michael Levashov, Ian R. Manchester, and Russ Tedrake. "Bounding on rough terrain with the LittleDog robot." International Journal of Robotics Research 30, no. 2 (2010): 192–215. http://dx.doi.org/10.1177/0278364910388315.

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A motion planning algorithm is described for bounding over rough terrain with the LittleDog robot. Unlike walking gaits, bounding is highly dynamic and cannot be planned with quasi-steady approximations. LittleDog is modeled as a planar five-link system, with a 16-dimensional state space; computing a plan over rough terrain in this high-dimensional state space that respects the kinodynamic constraints due to underactuation and motor limits is extremely challenging. Rapidly Exploring Random Trees (RRTs) are known for fast kinematic path planning in high-dimensional configuration spaces in the presence of obstacles, but search efficiency degrades rapidly with the addition of challenging dynamics. A computationally tractable planner for bounding was developed by modifying the RRT algorithm by using: (1) motion primitives to reduce the dimensionality of the problem; (2) Reachability Guidance, which dynamically changes the sampling distribution and distance metric to address differential constraints and discontinuous motion primitive dynamics; and (3) sampling with a Voronoi bias in a lower-dimensional “task space” for bounding. Short trajectories were demonstrated to work on the robot, however open-loop bounding is inherently unstable. A feedback controller based on transverse linearization was implemented, and shown in simulation to stabilize perturbations in the presence of noise and time delays.

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Silverberg, Larry M., and Jeffrey W. Eischen. "Theory of spacetime impetus." Physics Essays 34, no. 4 (2021): 548–63. http://dx.doi.org/10.4006/0836-1398-34.4.548.

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This article introduces the theory of spacetime impetus (SI). The theory unites Newtonian theory (NT) and the theory of general relativity (GR). To develop SI, we reformulated NT in spacetime and replaced the particle primitive in NT with the fragment of energy primitive in field theory. SI replaces Newton’s second law F = ma governing the motion of particles, where F, m, and a are, respectively, interaction force, mass, and acceleration, with the change equation P = k governing the motion of fragments of energy, where P and k are, respectively, action force and the curvature of a path in spacetime. To verify SI, we conducted three tests: Test 1 predicted the precession angles of Mercury and Jupiter, test 2 predicted the bending angle of light as it grazes the surface of the sun, and test 3 predicted the radius of the photon sphere. All three tests were in agreement with GR, the third corresponding to strong Riemannian curvature in GR. The equations of motion in SI are in terms of Cartesian coordinates and time and are relatively simple to solve. Undergraduate students in science and engineering and others with similar mathematical skills can validate the results for themselves.

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JIZBA, PETR, and FABIO SCARDIGLI. "STATISTICAL ORIGIN OF SPECIAL AND DOUBLY SPECIAL RELATIVITY." International Journal of Modern Physics: Conference Series 23 (January 2013): 373–78. http://dx.doi.org/10.1142/s201019451301163x.

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We show how a Brownian motion on a short scale can originate a relativistic motion on scales larger than particle's Compton wavelength. Special relativity appears to be not a primitive concept, but rather it statistically emerges when a coarse graining average over distances of order, or longer than the Compton wavelength is taken. Our scheme accommodates easily also the doubly special relativistic dynamics. A previously unsuspected, common statistical origin of the two frameworks is brought to light for the first time.

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Sosnik, Ronen, Eliyahu Chaim, and Tamar Flash. "Stopping is not an option: the evolution of unstoppable motion elements (primitives)." Journal of Neurophysiology 114, no. 2 (2015): 846–56. http://dx.doi.org/10.1152/jn.00341.2015.

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Stopping performance is known to depend on low-level motion features, such as movement velocity. It is not known, however, whether it is also subject to high-level motion constraints. Here, we report results of 15 subjects instructed to connect four target points depicted on a digitizing tablet and stop “as rapidly as possible” upon hearing a “stop” cue (tone). Four subjects connected target points with straight paths, whereas 11 subjects generated movements corresponding to coarticulation between adjacent movement components. For the noncoarticulating and coarticulating subjects, stopping performance was not correlated or only weakly correlated with motion velocity, respectively. The generation of a straight, point-to-point movement or a smooth, curved trajectory was not disturbed by the occurrence of a stop cue. Overall, the results indicate that stopping performance is subject to high-level motion constraints, such as the completion of a geometrical plan, and that globally planned movements, once started, must run to completion, providing evidence for the definition of a motion primitive as an unstoppable motion element.

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Huang, Jianbing, and Chia-Hsiang Menq. "Identification and Characterization of Regular Surfaces from Unorganized Points by Normal Sensitivity Analysis." Journal of Computing and Information Science in Engineering 2, no. 2 (2002): 115–24. http://dx.doi.org/10.1115/1.1509075.

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In this paper, the concept of free motion subspace is introduced and utilized to characterize the special kinematic properties of regular surfaces, including planes, natural quadrics, and regular swept surfaces. Based on the concept, a general approach is developed to automatically identify the surface type and calculate the associated geometric parameters of an unknown surface from unorganized measurement points. In the approach, a normal sensitivity matrix, that characterizes the normal perturbation of surface points under differential motions, is derived. With the normal sensitivity matrix, it is shown that the free motion subspace of a surface can be determined through a regular eigen analysis. From the identified free motion subspace, the surface type of a regular surface can be determined and its geometric parameters can be simultaneously computed. An algorithm that identifies the free motion subspace of an unknown surface from its unorganized sample points has been implemented. Experiments are carried out to investigate the robustness and efficiency of the developed algorithm. The developed algorithm can be used to solve various problems including geometric primitive classification and parameter estimation, regular swept surface reconstruction, geometric constraint recognition and multi-view data registration. Integrated with state-of-art segmentation techniques, the proposed method can be used for object recognition, robot vision, and reverse engineering.

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Lin, Yun-An, m. c. schraefel, Wei-Hung Chiang, and Kenneth J. Loh. "Wearable nanocomposite kinesiology tape for distributed muscle engagement monitoring." MRS Advances 6, no. 1 (2021): 6–13. http://dx.doi.org/10.1557/s43580-021-00005-4.

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AbstractSports coaches help athletes develop and improve their skills by assessing their ability to perform motion primitives that make up functional sports tasks. Sports coaching today is mostly done visually, which demands constant attention and can be imprecise. While sensors like electronic textiles and surface electromyography can measure muscle engagement, they are susceptible to movement artifacts and noise due to surface electrode issues. Therefore, the work reported here focuses on our development of self-adhesive, fabric-based sensors that can be directly affixed onto skin for monitoring skin-strains and distributed muscular engagement during functional movements. The vision is that these sensors can be readily used by sports coaches and individuals to better assess motion primitives and the execution of sports tasks. The approach integrates piezoresistive graphene nanosheet thin films with kinesiology tape (K-Tape). Because every location of the film is responsive to strains, electrodes can also be judiciously placed along the nanocomposite for distributed strain sensing. Nanocomposite or “Smart K-Tape” sensors were fabricated, and electromechanical tests were conducted to characterize their tensile, compressive, and cyclic sensing properties. Upon confirming their linearity, repeatability, stability, and high sensitivity, individuals wore Smart K-Tape sensors over different muscle groups as they performed prescribed exercise and stretching movements. The Smart K-Tapes outputted unique waveforms that revealed the speed and duration of muscular engagement through movement sequences. Furthermore, the region of muscular contraction could also be localized using each Smart K-Tape as a distributed strain sensor, which demonstrated promise as a convenient and quantitative motion primitive assessment tool relevant for sports coaching and athletic skills development.

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Long, J. H., A. C. Lammert, C. A. Pell, et al. "A Navigational Primitive: Biorobotic Implementation of Cycloptic Helical Klinotaxis in Planar Motion." IEEE Journal of Oceanic Engineering 29, no. 3 (2004): 795–806. http://dx.doi.org/10.1109/joe.2004.833233.

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Mathur, M. B. "Tropical storm motion and structure in a fine mesh primitive equation model." Meteorology and Atmospheric Physics 50, no. 1-3 (1992): 127–42. http://dx.doi.org/10.1007/bf01025509.

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Journal articles on the topic 'Primitive motion'

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