1,172 research outputs found

    A Convex Polynomial Force-Motion Model for Planar Sliding: Identification and Application

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    We propose a polynomial force-motion model for planar sliding. The set of generalized friction loads is the 1-sublevel set of a polynomial whose gradient directions correspond to generalized velocities. Additionally, the polynomial is confined to be convex even-degree homogeneous in order to obey the maximum work inequality, symmetry, shape invariance in scale, and fast invertibility. We present a simple and statistically-efficient model identification procedure using a sum-of-squares convex relaxation. Simulation and robotic experiments validate the accuracy and efficiency of our approach. We also show practical applications of our model including stable pushing of objects and free sliding dynamic simulations.Comment: 2016 IEEE International Conference on Robotics and Automation (ICRA

    Human-robot interaction for assistive robotics

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    This dissertation presents an in-depth study of human-robot interaction (HRI) withapplication to assistive robotics. In various studies, dexterous in-hand manipulation is included, assistive robots for Sit-To-stand (STS) assistance along with the human intention estimation. In Chapter 1, the background and issues of HRI are explicitly discussed. In Chapter 2, the literature review introduces the recent state-of-the-art research on HRI, such as physical Human-Robot Interaction (HRI), robot STS assistance, dexterous in hand manipulation and human intention estimation. In Chapter 3, various models and control algorithms are described in detail. Chapter 4 introduces the research equipment. Chapter 5 presents innovative theories and implementations of HRI in assistive robotics, including a general methodology of robotic assistance from the human perspective, novel hardware design, robotic sit-to-stand (STS) assistance, human intention estimation, and control

    Geometry-aware Manipulability Learning, Tracking and Transfer

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    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

    Estimation of objects’ inertial parameters, and their usage in robot grasping and manipulation

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    The subject of this thesis is the estimation of an object's inertial parameters by a robotic arm, and the exploitation of those parameters in the design of efficient manipulation criteria. The inertial parameters of objects describe the resistance of the object to an applied force, and dictate its motion. Research has shown that humans intuitively exploit them for their everyday manipulations. As humans are very capable of performing efficient manipulations, it is natural that robots should use the inertial parameters as well. Additionally, as the inertial parameters are not straightforward to calculate, there is the need for development of methods that can estimate them online. This thesis focuses on two directions, developing novel methods so that robots can accurately estimate the inertial parameters of an object, as well as developing manipulation criteria that can make robot task completion more efficient. The relevant literature is gathered, categorised and analytically described, and the innovation gaps are identified. The thesis offers novel research solutions on the problem of estimation of the inertial parameters with minimal robot interaction. The paradigm is shifted from the existing literature, and a data-driven estimation algorithm is introduced, that achieves accurate results with both simulated and real data. Additionally, the presented research is offering novel manipulation criteria that are affected by the object's inertial parameters. The results suggest that knowledge of the inertial parameters can make the robot tasks more power-efficient and safe to their surroundings. The core methodology is shown to be versatile to the robotic platform. Though most experiments are performed on a terrestrial robot, a numerical example is also shown for a space robot. The results of the thesis suggest that the developed methods can be used in various environments, with the most suitable being extreme environments where accuracy, efficiency and autonomy is required

    Estimating An Object’s Inertial Parameters By Robotic Pushing: A Data-Driven Approach

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    Estimating the inertial properties of an object can make robotic manipulations more efficient, especially in extreme environments. This paper presents a novel method of estimating the 2D inertial parameters of an object, by having a robot applying a push on it. We draw inspiration from previous analyses on quasi-static pushing mechanics, and introduce a data-driven model that can accurately represent these mechan- ics and provide a prediction for the object’s inertial parameters. We evaluate the model with two datasets. For the first dataset, we set up a V-REP simulation of seven robots pushing objects with large range of inertial parameters, acquiring 48000 pushes in total. For the second dataset, we use the object pushes from the MIT M-Cube lab pushing dataset. We extract features from force, moment and velocity measurements of the pushes, and train a Multi-Output Regression Random Forest. The experimental results show that we can accurately predict the 2D inertial parameters from a single push, and that our method retains this robust performance under various surface types

    Aerial Manipulators for Contact-based Interaction

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