Understanding Object Weight from Human and Humanoid Lifting Actions

Sciutti, Alessandra; Patane, Laura; Nori, Francesco; Sandini, Giulio

doi:10.1109/tamd.2014.2312399

Cited by 42 publications

(36 citation statements)

References 40 publications

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“…Additionally, humans process humanoid and human lifting actions in a similar manner. In line with this, it has been shown that observers are able to infer the weight of an unknown lifted object with the same accuracy both when looking at a human actor or at the iCub robot performing the lifting ( Sciutti et al, 2013b , 2014 ). These results expand previous studies that showed that other behavioral phenomena associated to motor resonance (i.e., the activation of the observer’s motor system during action perception) can generalize to humanoid robot observation, such as priming ( Liepelt et al, 2010 ) and motor interference ( Oztop et al, 2005 ).…”

Section: Necessary Robot Features To Investigate Human Ability To Reamentioning

confidence: 64%

Investigating the ability to read others’ intentions using humanoid robots

et al. 2015

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The ability to interact with other people hinges crucially on the possibility to anticipate how their actions would unfold. Recent evidence suggests that a similar skill may be grounded on the fact that we perform an action differently if different intentions lead it. Human observers can detect these differences and use them to predict the purpose leading the action. Although intention reading from movement observation is receiving a growing interest in research, the currently applied experimental paradigms have important limitations. Here, we describe a new approach to study intention understanding that takes advantage of robots, and especially of humanoid robots. We posit that this choice may overcome the drawbacks of previous methods, by guaranteeing the ideal trade-off between controllability and naturalness of the interactive scenario. Robots indeed can establish an interaction in a controlled manner, while sharing the same action space and exhibiting contingent behaviors. To conclude, we discuss the advantages of this research strategy and the aspects to be taken in consideration when attempting to define which human (and robot) motion features allow for intention reading during social interactive tasks.

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Section: Necessary Robot Features To Investigate Human Ability To Reamentioning

confidence: 64%

Investigating the ability to read others’ intentions using humanoid robots

et al. 2015

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“…Altogether, this line of research suggests that motor resonance responds to human-like artificial agents, albeit this effect being reduced compared with real humans in some cases [ 24 , 45 ]. In other cases [ 38 , 39 ] the motor/perceptual resonance effect was at the same level for a humanoid robot as for a human. Thus, whether the motor/perceptual resonance effect is reduced when observing a robot as compared to observing a human might depend on the type of robot, its kinematic profile [ 46 ] or the type of task being performed.…”

Section: Action–perception Couplingmentioning

confidence: 96%

“…For example, if participants have to judge the weight of boxes lifted by other people while lifting boxes themselves, the observed weights are under- or over-estimated depending on the weight of the participant's own box [ 37 ]. These effects were preserved when the humanoid robot iCub [ 8 ] was performing the lifting actions [ 38 , 39 ].…”

Section: Action–perception Couplingmentioning

confidence: 99%

Embodied artificial agents for understanding human social cognition

Wykowska

Chaminade

Cheng

2016

Phil. Trans. R. Soc. B

162

121

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In this paper, we propose that experimental protocols involving artificial agents, in particular the embodied humanoid robots, provide insightful information regarding social cognitive mechanisms in the human brain. Using artificial agents allows for manipulation and control of various parameters of behaviour, appearance and expressiveness in one of the interaction partners (the artificial agent), and for examining effect of these parameters on the other interaction partner (the human). At the same time, using artificial agents means introducing the presence of artificial, yet human-like, systems into the human social sphere. This allows for testing in a controlled, but ecologically valid, manner human fundamental mechanisms of social cognition both at the behavioural and at the neural level. This paper will review existing literature that reports studies in which artificial embodied agents have been used to study social cognition and will address the question of whether various mechanisms of social cognition (ranging from lower- to higher-order cognitive processes) are evoked by artificial agents to the same extent as by natural agents, humans in particular. Increasing the understanding of how behavioural and neural mechanisms of social cognition respond to artificial anthropomorphic agents provides empirical answers to the conundrum ‘What is a social agent?’

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“…Walking, running, lifting, and jumping are the most common human movements in daily life [1]. Consequently, controlling, understanding and recognizing human movements are important tasks for people in the fields of robotics, computer vision, ergonomics, and biomechanics [2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

An Improved Nonstationary Fuzzy System Approach versus Type-2 Fuzzy System for the Lifting Motion Control with Human-in-the-Loop Simulation

Karaköse¹

2018

IJCIS

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People working in the fields of robotics, animation, computer graphics and computer vision, and biomechanics, find it difficult to conduct human motion simulations, such as lifting, walking and running. This is because it is difficult to predict all of the motion strategies in a variety of situations. The human lifting motion is hard work; at the same time, lifting is the most demanded robotic motion. In this paper, a fuzzy integral based nonstationary fuzzy inference system is proposed to control a five-segment human model for the human lifting motion with human-inthe-loop simulation. This system was designed to reduce the computational complexity of nonstationary fuzzy systems. Hence, this paper contributes to the literature in two ways: as an improved nonstationary fuzzy system and as a human lifting motion simulation framework. A fuzzy integral algorithm between the fuzzification and defuzzification stages has been used to aggregate the inference outputs of the nonstationary fuzzy controller. The fuzzy integral algorithm uses fuzzy values obtained during the fuzzification stage as the attribute values and the fuzzy values obtained by a one-loop quantum particle swarm optimization algorithm as the importance values. The computational complexity in the nonstationary fuzzy systems and type-2 fuzzy systems can be reduced between 25 to 60 percent with the improved nonstationary fuzzy system. An experimental application of the human lifting motion was carried out to demonstrate the effectiveness of the proposed approach. The results illustrated that the proposed algorithm can achieve increased simplicity, improved effectiveness, good robustness, and a higher precision of computation.

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Understanding Object Weight from Human and Humanoid Lifting Actions

Cited by 42 publications

References 40 publications

Investigating the ability to read others’ intentions using humanoid robots

Investigating the ability to read others’ intentions using humanoid robots

Embodied artificial agents for understanding human social cognition

An Improved Nonstationary Fuzzy System Approach versus Type-2 Fuzzy System for the Lifting Motion Control with Human-in-the-Loop Simulation

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