Synthesis of variable dancing styles based on a compact spatiotemporal representation of dance

Dance as a complex expressive form of motion is able to convey emotion, meaning and social idiosyncrasies that opens channels for non-verbal communication, and promotes rich cross-modal interactions with music and the environment. As such, realistic dancing characters may incorporate crossmodal information and variability of the dance forms through compact representations that may describe the movement structure in terms of its spatial and temporal organization. In this paper, we propose a novel method for synthesizing beatsynchronous dancing motions based on a compact topological model of dance styles, previously captured with a motion capture system. The model was based on the Topological Gesture Analysis (TGA) which conveys a discrete three-dimensional point-cloud representation of the dance, by describing the spatiotemporal variability of its gestural trajectories into uniform spherical distributions, according to classes of the musical meter. The methodology for synthesizing the modeled dance traces back the topological representations, constrained with definable metrical and spatial parameters, into complete dance instances whose variability is controlled by stochastic processes that considers both TGA distributions and the kinematic constraints of the body morphology. In order to assess the relevance and flexibility of each parameter into feasibly reproducing the style of the captured dance, we correlated both captured and synthesized trajectories of samba dancing sequences in relation to the level of compression of the used model, and report on a subjective evaluation over a set of six tests. The achieved results validated our approach, suggesting that a periodic dancing style, and its musical synchrony, can be feasibly reproduced from a suitably parametrized discrete spatiotemporal representation of the gestural motion trajectories, with a notable degree of compression

Çok gövdeli sistemlerde hareket analizi

Çok göovdeli sistemlerin hareket analizi son yıllarda önemli bir araştırma konusu haline gelmiştir. Bunun sebebi performans analizi, otomatik güvenlik ve izleme sistemlerinin gerçeklenmesi, gerçekçi insan-makine arayüzlerinin oluşturulması, içerik tabanlı imge depolanması ve erişimi gibi motive edici uygulama alanlarının varlığıdır. Bu alanda çok sayıda çalışma yayınlanmış olsa da bu araştırmanın henüz geliştirilebilecek yönleri vardır. Bu çalışmada, çok gövdeli bir sistemin hareketini, sistemi her biri birer robotik kol şeklindeki çok sayıda alt sisteme ayrıştırarak incelemeyi öneriyoruz. Çok gövdeli bir sistemin hareketini tanımak için her bir robotik kolun eklemlerinden gelecek algılayıcı bilgisini, yani eklem açılarını kullanıyoruz. Önerilen yöntem herbir ayrıştırılmış parçanın periyodik hareketini analiz etmek için eklem açılarının birbirine göre çizdirilmesiyle elde edilen imza eğrilerini kullanmaktadır. Akt¨or ayırt etme ve aksaklık tesbiti örnekleri sunulmuş ve önerilen yöntem benzetimlerle doğrulanmıştır

Hierarchical Spatio-Temporal Morphable Models for Representation of complex movements for Imitation Learning

Imitation learning is a promising technique for teaching robots complex movement sequences. One key problem in this area is the transfer of perceived movement characteristics from perception to action. For the solution of this problem, representations are required that are suitable for the analysis and the synthesis of complex action sequences. We describe the method of Hierarchical Spatio-Temporal Morphable Models that allows an automatic segmentation of movements sequences into movement primitives, and a modeling of these primitives by morphing between a set of prototypical trajectories. We use HSTMMs in an imitation learning task for human writing movements. The models are learned from recorded trajectories and transferred to a human-like robot arm. Due to the generalization proper- ties of our movement representation, the arm is capable of synthesizing new writing movements with only a few learning examples

Coupled Human-machine Tele-manipulation

AbstractRobots are primarily deployed for tasks which are dirty, dull, or dangerous. While the former two are already highly automated, many dangerous tasks such as explosive ordnance disposal or inspection in hazardous environments are predominantly done via tele-operation. Usually, such tasks require the manipulation of objects in a way that cannot be done reliably with automated systems. In this paper, we present a method to tele-operate the manipulator of a robot by transferring the operator's arm movement. The movement is recorded with inertial measurement units which can be sewn into clothing and need no external infrastructure like cameras or motion capture systems. The lack of intermediate user interfaces (e.g. joysticks) makes this control method very intuitive and easy to learn. We demonstrate this with two different NIST manipulation tests and as part of an integrated system for the ELROB robot competition

IK-FA, a new heuristic inverse kinematics solver using firefly algorithm

In this paper, a heuristic method based on Firefly Algorithm is proposed for inverse kinematics problems in articulated robotics. The proposal is called, IK-FA. Solving inverse kinematics, IK, consists in finding a set of joint-positions allowing a specific point of the system to achieve a target position. In IK-FA, the Fireflies positions are assumed to be a possible solution for joints elementary motions. For a robotic system with a known forward kinematic model, IK-Fireflies, is used to generate iteratively a set of joint motions, then the forward kinematic model of the system is used to compute the relative Cartesian positions of a specific end-segment, and to compare it to the needed target position. This is a heuristic approach for solving inverse kinematics without computing the inverse model. IK-FA tends to minimize the distance to a target position, the fitness function could be established as the distance between the obtained forward positions and the desired one, it is subject to minimization. In this paper IK-FA is tested over a 3 links articulated planar system, the evaluation is based on statistical analysis of the convergence and the solution quality for 100 tests. The impact of key FA parameters is also investigated with a focus on the impact of the number of fireflies, the impact of the maximum iteration number and also the impact of (a, ß, ¿, d) parameters. For a given set of valuable parameters, the heuristic converges to a static fitness value within a fix maximum number of iterations. IK-FA has a fair convergence time, for the tested configuration, the average was about 2.3394 × 10-3 seconds with a position error fitness around 3.116 × 10-8 for 100 tests. The algorithm showed also evidence of robustness over the target position, since for all conducted tests with a random target position IK-FA achieved a solution with a position error lower or equal to 5.4722 × 10-9.Peer ReviewedPostprint (author's final draft