Geometry-aware Manipulability Learning, Tracking and Transfer

Body posture influences human and robots performance in manipulation tasks, as appropriate poses facilitate motion or force exertion along different axes. In robotics, manipulability ellipsoids arise as a powerful descriptor to analyze, control and design the robot dexterity as a function of the articulatory joint configuration. This descriptor can be designed according to different task requirements, such as tracking a desired position or apply a specific force. In this context, this paper presents a novel \emph{manipulability transfer} framework, a method that allows robots to learn and reproduce manipulability ellipsoids from expert demonstrations. The proposed learning scheme is built on a tensor-based formulation of a Gaussian mixture model that takes into account that manipulability ellipsoids lie on the manifold of symmetric positive definite matrices. Learning is coupled with a geometry-aware tracking controller allowing robots to follow a desired profile of manipulability ellipsoids. Extensive evaluations in simulation with redundant manipulators, a robotic hand and humanoids agents, as well as an experiment with two real dual-arm systems validate the feasibility of the approach.Comment: Accepted for publication in the Intl. Journal of Robotics Research (IJRR). Website: https://sites.google.com/view/manipulability. Code: https://github.com/NoemieJaquier/Manipulability. 24 pages, 20 figures, 3 tables, 4 appendice

Riemannian geometry as a unifying theory for robot motion learning and control

Riemannian geometry is a mathematical field which has been the cornerstone of revolutionary scientific discoveries such as the theory of general relativity. Despite early uses in robot design and recent applications for exploiting data with specific geometries, it mostly remains overlooked in robotics. With this blue sky paper, we argue that Riemannian geometry provides the most suitable tools to analyze and generate well-coordinated, energy-efficient motions of robots with many degrees of freedom. Via preliminary solutions and novel research directions, we discuss how Riemannian geometry may be leveraged to design and combine physically-meaningful synergies for robotics, and how this theory also opens the door to coupling motion synergies with perceptual inputs.Comment: Published as a blue sky paper at ISRR'22. 8 pages, 2 figures. Video at https://youtu.be/XblzcKRRIT

La réalité augmentée et virtuelle dans les destinations suisses: analyse de leurs cas d’utilisation et recommandations

Ce travail de Bachelor est consacré à l’utilisation de la réalité augmentée et virtuelle dans les destinations ainsi que chez les prestataires touristiques suisses. Il vise à recenser tous les cas d’utilisation, à les analyser via des catégories afin de pouvoir esquisser un portait géographique et typologique de ces technologies dans le paysage touristique suisse

Mixed scalar-pseudoscalar Higgs boson production through next-to-next-to-leading order at the LHC

We study the production of a mixed scalar-pseudoscalar Higgs boson in gluon fusion at the LHC, through next-to-next-to-leading order (NNLO) in QCD. We obtain fully differential results, including the decay of the Higgs boson to two charged lepton pairs. We discuss the impact of the interference between the scalar and pseudoscalar states. We also show differential distributions for several kinematic variables whose shape is sensitive to the parity of the Higgs boson, and assess the impact of the NNLO QCD corrections on these shapes

Two-loop splitting amplitudes and the single-real contribution to inclusive Higgs production at N3LO

The factorisation of QCD matrix elements in the limit of two external partons becoming collinear is described by process-independent splitting amplitudes, which can be expanded systematically in perturbation theory. Working in conventional dimensional regularisation, we compute the two-loop splitting amplitudes for all simple collinear splitting processes, including subleading terms in the regularisation parameter. Our results are then applied to derive an analytical expression for the two-loop single-real contribution to inclusive Higgs boson production in gluon fusion to fourth order (N3LO) in perturbative QCD.Comment: 71 page

Analysis and Transfer of Human Movement Manipulability in Industry-like Activities

Humans exhibit outstanding learning, planning and adaptation capabilities while performing different types of industrial tasks. Given some knowledge about the task requirements, humans are able to plan their limbs motion in anticipation of the execution of specific skills. For example, when an operator needs to drill a hole on a surface, the posture of her limbs varies to guarantee a stable configuration that is compatible with the drilling task specifications, e.g. exerting a force orthogonal to the surface. Therefore, we are interested in analyzing the human arms motion patterns in industrial activities. To do so, we build our analysis on the so-called manipulability ellipsoid, which captures a posture-dependent ability to perform motion and exert forces along different task directions. Through thorough analysis of the human movement manipulability, we found that the ellipsoid shape is task dependent and often provides more information about the human motion than classical manipulability indices. Moreover, we show how manipulability patterns can be transferred to robots by learning a probabilistic model and employing a manipulability tracking controller that acts on the task planning and execution according to predefined control hierarchies.Comment: Accepted for publication in IROS'20. Website: https://sites.google.com/view/manipulability/home . Video: https://youtu.be/q0GZwvwW9A