Whole-Body Human Inverse Dynamics with Distributed Micro-Accelerometers, Gyros and Force Sensing

The success of robots in real-world environments is largely dependent on their ability to interact with both humans and said environment. The FP7 EU project CoDyCo focused on the latter of these two challenges by exploiting both rigid and compliant contacts dynamics in the robot control problem. Regarding the former, to properly manage interaction dynamics on the robot control side, an estimation of the human behaviours and intentions is necessary. In this paper we present the building blocks of such a human-in-the-loop controller, and validate them in both simulation and on the iCub humanoid robot using a human-robot interaction scenario. In this scenario, a human assists the robot in standing up from being seated on a bench. Index Terms-Physical Human-Robot Interaction, Humanoid Robots I. INTRODUCTION T HE ability to interact with and manipulate the environment gives robots a distinct advantage over purely software based automated agents. In the FP7 European project, CoDyCo, the focus was on how to properly exploit contact dynamics in the control of the robot. When the interaction involves humans, their intrinsic unpredictability makes the collaboration problem far more difficult. Foreseen robotic applications range from the use of robots as service and elderly assistants, to their use in industrial plants in close contact with

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“…We thus perform a Maximum A-Posteriori (MAP) estimation. We refer the reader to [25], [26] for a more thorough description of the method.…”

Section: Real-time Estimation Of Humans Dynamicsmentioning

confidence: 99%

The CoDyCo Project Achievements and Beyond: Toward Human Aware Whole-Body Controllers for Physical Human Robot Interaction

Romanò

Nava

Azad

et al. 2018

IEEE Robot. Autom. Lett.

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“…In this section we briefly recall the probabilistic method for estimating dynamic variables of an articulated mechanical system by exploiting the so-called sensor fusion information, already presented in our previous work [the reader should refer to Latella et al (2016) for a more thorough description]. The following description is applied within the context of the fixed-base representation of equations in Sect.…”

Section: Probabilistic Sensor Fusion Algorithmmentioning

confidence: 99%

“…The paper is built on the theoretical framework described in our previous work (Latella et al 2016) from which it inherits both the notation and formulation. This paper represents the real-time evolution of Latella et al (2016) and a first attempt at advancing the current state of the art in pHRI by designing an estimation tool for monitoring the dynamics of the human while physically interacting with a robot, in real-time.…”

Section: Introductionmentioning

confidence: 99%

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Towards real-time whole-body human dynamics estimation through probabilistic sensor fusion algorithms

et al. 2018

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Physical human-robot interaction is receiving a growing attention from the scientific community. One of the main challenges is to understand the principles governing the mutual behaviour during collaborative interactions between humans. In this context, the knowledge of human whole-body motion and forces plays a pivotal role. Current state of the art methods, however, do not allow one for reliable estimations of the human dynamics during physical human-robot interaction. This paper builds upon our former work on human dynamics estimation by proposing a probabilistic framework and an estimation tool for online monitoring of the human dynamics during human-robot collaboration tasks. The soundness of the proposed approach is verified in human-robot collaboration experiments and the results show that our probabilistic framework is able to estimate the human dynamic, thereby laying the foundation for more complex collaboration scenarios.

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Investigating rolling as mechanism for humeral fractures in non-ambulant infants: a preliminary finite element study

Altai

Viceconti

et al. 2020

Clinical Radiology

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tomography (CT)-based finite element models to quantitatively investigate the likelihood of self-inflicted humeral fracture in non-ambulant infants secondary to rolling. MATERIALS AND METHODS: Three whole-body post-mortem CT examinations of children at the age of rolling (two 4-month-old and one 6-month-old) were used. The mechanical moment needed by each infant to perform a rolling manoeuvre was calculated and applied to the finite element model in order to simulate spontaneous rolling from the prone to the supine position. RESULTS: The maximum predicted strains were found to be substantially lower (with a difference of >80%) than the elastic limit of the bone. CONCLUSION: Results of this study challenge the plausibility of self-inflicted humeral fracture caused by rolling in non-ambulant infants and indicate that it is unlikely for a humeral fracture to result from this mechanism without the assistance of an external force.

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Whole-Body Human Inverse Dynamics with Distributed Micro-Accelerometers, Gyros and Force Sensing

Cited by 11 publications

References 13 publications

The CoDyCo Project Achievements and Beyond: Toward Human Aware Whole-Body Controllers for Physical Human Robot Interaction

The CoDyCo Project Achievements and Beyond: Toward Human Aware Whole-Body Controllers for Physical Human Robot Interaction

Towards real-time whole-body human dynamics estimation through probabilistic sensor fusion algorithms

Investigating rolling as mechanism for humeral fractures in non-ambulant infants: a preliminary finite element study

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