Exploiting Redundancy to Facilitate Physical Interaction

Hermus, James; Lachner, Johannes; Verdi, David; Hogan, Neville

doi:10.1109/tro.2021.3086632

Cited by 18 publications

(18 citation statements)

References 82 publications

(97 reference statements)

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“…However, redundant degrees of freedom may be controlled by superposition of mechanical impedance primitives. Remarkably, unlike optimization-based methods, as the number of redundant degrees of freedom increased, control based on the superposition of impedance primitives improved; in effect, with greater redundancy control became easier [38].…”

Section: Discussionmentioning

confidence: 99%

Dynamic Primitives Limit Human Force Regulation During Motion

West

Hermus

Huber

et al. 2022

IEEE Robot. Autom. Lett.

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Humans excel at physical interaction despite long feedback delays and low-bandwidth actuators. Yet little is known about how humans manage physical interaction. A quantitative understanding of how they do is critical for designing machines that can safely and effectively interact with humans, e.g. amputation prostheses, assistive exoskeletons, therapeutic rehabilitation robots, and physical human-robot collaboration. To facilitate applications, this understanding should be in the form of a simple mathematical model that not only describes humans' capabilities but also their limitations. In robotics, hybrid control allows simultaneous, independent control of both motion and force and it is often assumed that humans can modulate force independent of motion as well. This paper experimentally tested that assumption.Participants were asked to apply a constant 5N force on a robot manipulandum that moved along an elliptical path. After initial improvement, force errors quickly plateaued, despite practice and visual feedback. Within-trial analyses revealed that force errors varied with position on the ellipse, rejecting the hypothesis that humans have independent control of force and motion. The findings are consistent with a feed-forward motion command composed of two primitive oscillations acting through mechanical impedance to evoke force.

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Section: Discussionmentioning

confidence: 99%

Dynamic Primitives Limit Human Force Regulation During Motion

West

Hermus

Huber

et al. 2022

IEEE Robot. Autom. Lett.

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“…By comparing (11) and (12) with the passivity condition of (10), and considering the boundedness of U g (q), it can be concluded that the controller is strictly output passive with respect to the interaction port ( q, −τ imp ).…”

Section: B Passivity Property Of the Controller For Passive Environmentsmentioning

confidence: 99%

“…This has the consequence that the desired endeffector behavior might suffer. It was shown in [10] that the conflict can be minimized if the robot has enough DOF.…”

mentioning

confidence: 99%

Shaping Impedances to Comply With Constrained Task Dynamics

2022

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Humans are capable of managing multiple tasks simultaneously. It is widely assumed that human motor control can be emulated by impedance control. To achieve human-like behavior, however, the impedance parameters of multiple tasks may vary during task execution. We propose an algorithm that shapes task impedance as a function of the robot's time-varying inertial properties. These properties involve virtually constrained masses and virtually constrained inertias that counteract a task in order to comply with a given constraint. In this work, we not only detect task conflicts, but also show how to handle them. Our method is able to control kinematically redundant robots. We developed a dampingdesign method that does not interfere with our desired Cartesian task-space behavior. The control approach was verified in experiments on a real robot. We compared our impedance shaping method with two alternative control approaches: simple impedance superposition and nullspace projection. Our method preserved the passivity while improving the Cartesian task performance of an impedance controller. The method has computational advantages, beneficial to control robots with many degrees of freedom.

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“…, the initial guess of the independent coordinates for the high-fidelity HAMM, is given by ind s init = K qq (6) where K is a sparse matrix given by 44 1 1 11…”

Section: Hybrid Ik Algorithmmentioning

confidence: 99%

“…In such case natural human arm postures have to be replicated autonomously. This challenge mainly results from the redundant degrees of freedom (DoFs) and unknown postural control strategy of human arms [6].…”

Section: Introductionmentioning

confidence: 99%

Muscle-Effort-Minimization-Inspired Kinematic Redundancy Resolution for Replicating Natural Posture of Human Arm

Xia

Wang

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Replicating natural postures of human arms is essential to generate human-like behaviors in robotic applications for humans nearby. However, how to realize this requirement in interactive scenarios remains a challenge due to the kinematic redundancy and unknown postural control strategy of human arms. Inspired by the physiological characteristics that the musculoskeletal system is coordinated to minimize muscle effort in human behaviors, this paper aims to address the issue by solving a muscle effort minimization problem. It adopts a high-fidelity human arm musculoskeletal model (HAMM) and considers the implicit constraint (desired hand pose) and the inequality constraints (range of joint motion). The constrained minimization is in general nonconvex, consequently sensitive to initial guesses in iterative procedures. So, it is impracticable to solve it directly with existing gradient-based deterministic approaches or standard evolutionary algorithms. As the main contribution, a hybrid inverse kinematics algorithm was proposed for the HAMM with 7 independent and 13 mimic joints to obtain the feasible arm postures satisfying the minimization constraints. Using the arm swivel angle that parametrizes the kinematic redundancy of the HAMM, geometrically equidistant initial guess candidates can be generated over the 1-dimension feasible posture manifold. As another contribution, we present a two-phase global minimization algorithm to handle the nonconvexity of the constrained minimization. It consists of a local-search phase on the null-space of the geometric Jacobian matrix and a global-search phase with an initial guess resampling strategy. The proposed approach was validated by replicating the natural arm postures of 5 right-handed subjects in daily tasks.

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Exploiting Redundancy to Facilitate Physical Interaction

Cited by 18 publications

References 82 publications

Dynamic Primitives Limit Human Force Regulation During Motion

Dynamic Primitives Limit Human Force Regulation During Motion

Shaping Impedances to Comply With Constrained Task Dynamics

Muscle-Effort-Minimization-Inspired Kinematic Redundancy Resolution for Replicating Natural Posture of Human Arm

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