Latest Papers

ASME Journal of Mechanisms and Robotics

  • Intuitive Physical Human–Robot Interaction Using an Underactuated Redundant Manipulator With Complete Spatial Rotational Capabilities
    by Audet JM, Gosselin C. on July 21, 2021 at 12:00 am

    AbstractIn this paper, the concept of underactuated redundancy is presented using a novel spatial two-degrees-of-freedom (2-DoF) gravity-balanced rotational manipulator, composed of movable counterweights. The proposed kinematic arrangement makes it possible to intuitively manipulate a payload undergoing 3-DoF spatial rotations by adding a third rotational axis oriented in the direction of gravity. The static equilibrium equations of the 2-DoF architecture are first described in order to provide the required configuration of the counterweights for a statically balanced mechanism. A method for calibrating the mechanism, which establishes the coefficients of the static equilibrium equations, is also presented. In order to both translate and rotate the payload during manipulation, the rotational manipulator is mounted on an existing translational manipulator. Experimental validations of both systems are presented to demonstrate the intuitive and responsive behavior of the manipulators during physical human–robot interactions.

  • Special Section: Mobile Robots and Unmanned Ground Vehicles
    by Reina G, Das TK, Quaglia G, et al. on July 21, 2021 at 12:00 am

    Inspired by the fifth-year anniversary celebration of the homonymous symposium at the International Mechanical Engineering Congress & Exposition (IMECE), this Special Section with ten articles shares the latest research efforts in design, theory, development, and applications for mobile robots and unmanned ground vehicles.

Multiparameter Real-World System Identification Using Iterative Residual Tuning

Abstract

In this work, we consider the problem of nonlinear system identification using data to learn multiple and often coupled parameters that allow a simulator to more accurately model a physical system or mechanism and close the so-called reality gap for more accurate robot control. Our approach uses iterative residual tuning (IRT), a recently developed derivative-free system identification technique that utilizes neural networks and visual observation to estimate parameter differences between a proposed model and a target model. We develop several modifications to the basic IRT approach and apply it to the system identification of a five-parameter model of a marble rolling in a robot-controlled labyrinth game mechanism. We validate our technique both in simulation—where we outperform two baselines—and on a real system, where we achieve marble tracking error of 4% after just five optimization iterations.
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