The where of handovers by humans: Effect of partner characteristics, distance and visual feedback

Kato, Saki; Yamanobe, Natsuki; Venture, Gentiane; Yoshida, Eiichi; Ganesh, Gowrishankar

doi:10.1371/journal.pone.0217129

Cited by 14 publications

(8 citation statements)

References 27 publications

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“…Humans are so well trained on handing over objects, that those handovers are successful with all kinds of objects like tools, keys or ropes. Irrespective of the exact properties of an object and the experience and skilfulness of person we are interacting with, human-to-human handovers have an astonishing rate of success, while being considered highly automated at the same time 1 . One crucial element in this particular skill is the human capability to adapt grip-forces to a wide range of different tasks and objects 2 , 3 .…”

Section: Introductionmentioning

confidence: 99%

Grip-force modulation in human-to-human object handovers: effects of sensory and kinematic manipulations

Döhring

Müller

Joch

2020

Sci Rep

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From a motor control perspective, human-to-human object handovers can be described as coordinated joint-actions transferring the power over an object from a passer to a receiver. Although, human-to-human handovers are very reliable in terms of success, it is unclear how both actors plan and execute their actions independently while taking into account the partners behaviour. Here, we measured grip-forces of passer and receiver while handing over an object. In order to study mutual interaction in human-to-human handovers, we measured how changes in relevant features (sensory information available to the passer and receiver’s reaching velocity) in one partner affect grip-force profiles not only at the manipulated side but also at the partner’s side. The data reveals strong effects of sensory manipulations on time-related (duration and release delay) and dynamometric measures (force rates). Variation of reaching velocities had the largest impact on the receiver’s force rates. Furthermore, there are first indications that the vertical object movement is used as an implicit cue to signal the start of the handover in situations where vision is restricted.

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Section: Introductionmentioning

confidence: 99%

Grip-force modulation in human-to-human object handovers: effects of sensory and kinematic manipulations

Döhring

Müller

Joch

2020

Sci Rep

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“…For the third-person perspective, the visual system which analyzes the human-human handover process can be used for demonstration of robot-related tasks, such as human-robot handover [32,26], robot-human handover [4,21,27], dexterous manipulation [22,23], etc. Besides, it can also serve as a analysis tool for cognitive study on human behaviour understanding [20,8].…”

Section: Perception In Handover Taskmentioning

confidence: 99%

“…1). It is not only a rich area for the robotics community [4] and the cognitive community [20], but also for the computer vision community. Various perception behaviours are involved in this process, including but not limited to object detection (where to find the object) [24,18], visual grasp prediction (how to grasp the object) [9], pre-handover reconstruction (how the giver interact with the object during passing over) [16,28] and receiver grasp prediction (how the receiver obtain the object).…”

Section: Introductionmentioning

confidence: 99%

H2O: A Benchmark for Visual Human-human Object Handover Analysis

Ye¹,

Xu²,

Xue³

et al. 2021

Preprint

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Object handover is a common human collaboration behavior that attracts attention from researchers in Robotics and Cognitive Science. Though visual perception plays an important role in the object handover task, the whole handover process has been specifically explored. In this work, we propose a novel rich-annotated dataset, H2O, for visual analysis of human-human object handovers. The H2O, which contains 18K video clips involving 15 people who hand over 30 objects to each other, is a multi-purpose benchmark. It can support several vision-based tasks, from which, we specifically provide a baseline method, RGPNet, for a less-explored task named Receiver Grasp Prediction. Extensive experiments show that the RGPNet can produce plausible grasps based on the giver's hand-object states in the pre-handover phase. Besides, we also report the hand and object pose errors with existing baselines and show that the dataset can serve as the video demonstrations for robot imitation learning on the handover task. Dataset, model and code will be made public.

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“…A key challenge is choosing parameters of the robot's actions to optimize for a fluent handover. This includes the choice of object pose and robot's grasp on the object, taking into account user comfort [17], preferences based on subjective feedback [18], affordances and intended use of the objects after the handover [19], [20], [21], [22], [23], motion constraints of the human [13], social role of the human [24], and configuration of the object when being grasped before the handover [25]. Other work emphasizes parameters of the trajectory to reach the handover pose, exploring the approach angle [11], starting pose of trajectory in contrast to the handover pose [15], motion smoothness [26], object release time [27], estimated human wrist pose [28], [29], relative timing of handover phases [30], and ergonomic preferences of humans [31].…”

Section: Related Workmentioning

confidence: 99%

Human Grasp Classification for Reactive Human-to-Robot Handovers

Yang

Paxton

Çakmak

et al. 2020

Preprint

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Transfer of objects between humans and robots is a critical capability for collaborative robots. Although there has been a recent surge of interest in human-robot handovers, most prior research focus on robot-to-human handovers. Further, work on the equally critical human-to-robot handovers often assumes humans can place the object in the robot's gripper. In this paper, we propose an approach for human-to-robot handovers in which the robot meets the human halfway, by classifying the human's grasp of the object and quickly planning a trajectory accordingly to take the object from the human's hand according to their intent. To do this, we collect a human grasp dataset which covers typical ways of holding objects with various hand shapes and poses, and learn a deep model on this dataset to classify the hand grasps into one of these categories. We present a planning and execution approach that takes the object from the human hand according to the detected grasp and hand position, and replans as necessary when the handover is interrupted. Through a systematic evaluation, we demonstrate that our system results in more fluent handovers versus two baselines. We also present findings from a user study (N = 9) demonstrating the effectiveness and usability of our approach with naive users in different scenarios. More results and videos can be found at http://wyang.me/handovers.

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The where of handovers by humans: Effect of partner characteristics, distance and visual feedback

Cited by 14 publications

References 27 publications

Grip-force modulation in human-to-human object handovers: effects of sensory and kinematic manipulations

Grip-force modulation in human-to-human object handovers: effects of sensory and kinematic manipulations

H2O: A Benchmark for Visual Human-human Object Handover Analysis

Human Grasp Classification for Reactive Human-to-Robot Handovers

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