Determining proper grasp configurations for handovers through observation of object movement patterns and inter-object interactions during usage

Chan, Wesley P.; Kakiuchi, Yohei; Okada, Kei; Inaba, Masayuki

doi:10.1109/iros.2014.6942733

Cited by 19 publications

(18 citation statements)

References 16 publications

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“…) grasping planar faces of object M SELF S 9 (Carvalho & Nolfi, 2016) traversability depth, haptic M SELF S 10 (Castellini et al, 2011) grasping SIFT BoW, contact joints M S B 11 (Çelikkanat et al, 2015) pushing, grasping, throwing, shaking depth, haptic, proprioceptive and audio M SEMI RR 12 (Chan et al, 2014) grasping pose, action-object relation M U RR 13 (Chang, 2015) cutting, painting edges, TSSC N S RR 14 (Chen et al, 2015) traversability RGB images, motor controls M S S 15 (Chu et al, 2016a) (Sinapov & Stoytchev, 2007) pulling, dragging changes in raw pixels M SELF S 113 (Sinapov & Stoytchev, 2008) pulling, dragging raw pixels, trajectories M SELF S 114 (Song et al, 2016) …”

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confidence: 99%

Computational models of affordance in robotics: a taxonomy and systematic classification

et al. 2017

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J. J. Gibson's concept of affordance, one of the central pillars of ecological psychology, is a truly remarkable idea that provides a concise theory of animal perception predicated on environmental interaction. It is thus not surprising that this idea has also found its way into robotics research as one of the underlying theories for action perception. The success of the theory in this regard has meant that existing research is both abundant and diffuse by virtue of the pursuit of multiple different paths and techniques with the common goal of enabling robots to learn, perceive and act upon affordances. Up until now there has existed no systematic investigation of existing work in this field. Motivated by this circumstance, in this article we begin by defining a taxonomy for computational models of affordances rooted in a comprehensive analysis of the most prominent theoretical ideas of import in the field. Subsequently, after performing a systematic literature review, we provide a classification of existing research within our proposed taxonomy. Finally, by both quantitatively and qualitatively assessing the data resulting from the classification process, we highlight gaps in the research terrain and outline open questions for the investigation of affordances in robotics that we believe will help inform future work, prioritize research goals, and potentially advance the field towards greater robot autonomy.Prepared using sagej.cls [Version: 2015/06/09 v1.01]

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confidence: 99%

Computational models of affordance in robotics: a taxonomy and systematic classification

et al. 2017

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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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“…Typical examples here can be found in Human-Robot Interaction research; for instance, robots that learn to recognize gestures and anticipate human actions (Saponaro et al, 2013 ; Jiang and Saxena, 2014 ). In Chan et al ( 2014 ), a robot learns proper grasp configurations for object handover by observing humans using tools such as knives and screwdrivers, in Shu et al ( 2017 ), a robot learns social affordances e.g., human-like behaviors, in human-robot interaction scenarios such as waving, shaking hands .…”

Section: Affordance Learning and Perceptionmentioning

confidence: 99%

Geometric Affordance Perception: Leveraging Deep 3D Saliency With the Interaction Tensor

Ochoa

Mayol-Cuevas

2020

Front. Neurorobot.

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Agents that need to act on their surroundings can significantly benefit from the perception of their interaction possibilities or affordances. In this paper we combine the benefits of the Interaction Tensor, a straight-forward geometrical representation that captures multiple object-scene interactions, with deep learning saliency for fast parsing of affordances in the environment. Our approach works with visually perceived 3D pointclouds and enables to query a 3D scene for locations that support affordances such as sitting or riding, as well as interactions for everyday objects like the where to hang an umbrella or place a mug. Crucially, the nature of the interaction description exhibits one-shot generalization. Experiments with numerous synthetic and real RGB-D scenes and validated by human subjects, show that the representation enables the prediction of affordance candidate locations in novel environments from a single training example. The approach also allows for a highly parallelizable, multiple-affordance representation, and works at fast rates. The combination of the deep neural network that learns to estimate scene saliency with the one-shot geometric representation aligns well with the expectation that computational models for affordance estimation should be perceptually direct and economical.

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Determining proper grasp configurations for handovers through observation of object movement patterns and inter-object interactions during usage

Cited by 19 publications

References 16 publications

Computational models of affordance in robotics: a taxonomy and systematic classification

Computational models of affordance in robotics: a taxonomy and systematic classification

Human Grasp Classification for Reactive Human-to-Robot Handovers

Geometric Affordance Perception: Leveraging Deep 3D Saliency With the Interaction Tensor

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