Autonomous Object Handover Using Wrist Tactile Information

Konstantinova, Jelizaveta; Krivić, Senka; Stilli, Agostino; Piater, Justus; Althoefer, Kaspar

doi:10.1007/978-3-319-64107-2_35

Cited by 13 publications

(8 citation statements)

References 12 publications

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“…Table 2 summarizes some works that use specific objective metrics, such as success rate [70,77,89,108,109,[113][114][115], interaction force [72,90,91,116,117], timings [70,80,87,110,114,[118][119][120][121], joint effort [109] and subjective metrics, such as fluency [70,80,87,110,114,[122][123][124], satisfaction [109,113,115,123,125,126], comfort [91,109,115,121,124,126,127], usage of interaction [90,109,113,115,125,…”

Section: Metrics In Hrcmentioning

confidence: 99%

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Castro

Silva

Santos

2021

Sensors

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Repetitive industrial tasks can be easily performed by traditional robotic systems. However, many other works require cognitive knowledge that only humans can provide. Human-Robot Collaboration (HRC) emerges as an ideal concept of co-working between a human operator and a robot, representing one of the most significant subjects for human-life improvement.The ultimate goal is to achieve physical interaction, where handing over an object plays a crucial role for an effective task accomplishment. Considerable research work had been developed in this particular field in recent years, where several solutions were already proposed. Nonetheless, some particular issues regarding Human-Robot Collaboration still hold an open path to truly important research improvements. This paper provides a literature overview, defining the HRC concept, enumerating the distinct human-robot communication channels, and discussing the physical interaction that this collaboration entails. Moreover, future challenges for a natural and intuitive collaboration are exposed: the machine must behave like a human especially in the pre-grasping/grasping phases and the handover procedure should be fluent and bidirectional, for an articulated function development. These are the focus of the near future investigation aiming to shed light on the complex combination of predictive and reactive control mechanisms promoting coordination and understanding. Following recent progress in artificial intelligence, learning exploration stand as the key element to allow the generation of coordinated actions and their shaping by experience.

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Section: Metrics In Hrcmentioning

confidence: 99%

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Castro

Silva

Santos

2021

Sensors

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“…The same control strategy can also be applied to an under-actuated hand, using linear models leveraging force readings from the elbow of the robot [190]. Moreover, the feedback from a force sensor mounted on the robot's wrist can be robustly used to modulate the release of an object [191].…”

Section: A Grip Force Modulationmentioning

confidence: 99%

Object Handovers: a Review for Robotics

Ortenzi¹,

Cosgun²,

Pardi³

et al. 2020

Preprint

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“…Konstantinova et al aim to address the challenge of handing arbitrary objects, based on a method that relies only on wrist force sensors (i.e., no vision or tactile information), but still requires the person to bring the object in contact with the robot gripper [18]. Most closely related to this paper is recent work by Rosenberg et al who developed a method for grasping objects that can be recognized by the robot from a human giver's hand [5].…”

Section: Introductionmentioning

confidence: 99%

Reactive Human-to-Robot Handovers of Arbitrary Objects

Yang

Paxton

Mousavian

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Human-robot object handovers have been an actively studied area of robotics over the past decade; however, very few techniques and systems have addressed the challenge of handing over diverse objects with arbitrary appearance, size, shape, and rigidity. In this paper, we present a visionbased system that enables reactive human-to-robot handovers of unknown objects. Our approach combines closed-loop motion planning with real-time, temporally-consistent grasp generation to ensure reactivity and motion smoothness. Our system is robust to different object positions and orientations, and can grasp both rigid and non-rigid objects. We demonstrate the generalizability, usability, and robustness of our approach on a novel benchmark set of 26 diverse household objects, a user study with naive users (N=6) handing over a subset of 15 objects, and a systematic evaluation examining different ways of handing objects. More results and videos can be found at https://sites.google.com/nvidia.com/ handovers-of-arbitrary-objects.

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Autonomous Object Handover Using Wrist Tactile Information

Cited by 13 publications

References 12 publications

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Object Handovers: a Review for Robotics

Reactive Human-to-Robot Handovers of Arbitrary Objects

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