Human Grasp Classification for Reactive Human-to-Robot Handovers

Yang, Wei; Paxton, Chris; Çakmak, Maya; Fox, Dieter

doi:10.1109/iros45743.2020.9341004

Cited by 45 publications

(29 citation statements)

References 44 publications

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“…Gualtieri et al ( 2017 ) and Zhang and Demiris ( 2020 ) propose robot systems to assist disabled people grab objects and dress cloths. Llopart et al ( 2017 ), Zhou et al ( 2019 ), Yang et al ( 2020 ) and Zeng et al ( 2020 ) aim to learn grasping capability to accomplish opening doors, grabbing glasses, picking objects from human's hands and throwing arbitrary objects. More interestingly, Parhar et al ( 2018 ), Guo N. et al ( 2020 ), and Kang et al ( 2020 ) utilizes robot grasp ability to help completing crops harvesting in the farm.…”

Section: Applicationsmentioning

confidence: 99%

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning

et al. 2021

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Dexterous manipulation, especially dexterous grasping, is a primitive and crucial ability of robots that allows the implementation of performing human-like behaviors. Deploying the ability on robots enables them to assist and substitute human to accomplish more complex tasks in daily life and industrial production. A comprehensive review of the methods based on point cloud and deep learning for robotics dexterous grasping from three perspectives is given in this paper. As a new category schemes of the mainstream methods, the proposed generation-evaluation framework is the core concept of the classification. The other two classifications based on learning modes and applications are also briefly described afterwards. This review aims to afford a guideline for robotics dexterous grasping researchers and developers.

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Section: Applicationsmentioning

confidence: 99%

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning

et al. 2021

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“…Moreover, non-verbal communication could be used through physical properties for a human intention understanding [86]. Some other studies have taken human safety into a big consideration, developing grasping selection and reaction systems that avoid the collision with the human hand [87][88][89].…”

Section: Object Handovermentioning

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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“…Hence, the robot directly tracks the object rather than the human hand, and no additional markers are needed, differently from the marker-based solutions proposed by Medina et al (2016) and by Pan et al (2018). Among the marker-less approaches, recent works are (Nemlekar et al, 2019;Yang et al, 2020;Rosenberger et al, 2021). Each of these works on H2R handover, and our paper, use object and/or human tracking with a different aim.…”

Section: Introductionmentioning

confidence: 99%

“…Each of these works on H2R handover, and our paper, use object and/or human tracking with a different aim. Nemlekar et al (2019) focus on reaching the handover location by tracking the human skeleton and predicting the so-called Object Transfer Point, Yang et al (2020) focus on the choice of the grasp orientation based on the human grasp type, while Rosenberger et al (2021) focus on the choice of a grasp that ensures the safety of the human using eye-in-hand vision. Our approach, instead, aims at reaching a grasp selected beforehand based on the task that the robot has to perform after the handover, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…placing the object on the shelf possibly using in-hand manipulation which requires specific constraints on the grasp. Differently from the solution by Yang et al (2020), in our work the grasp motion is not planned but directly executed in a closed-loop fashion, this allows the robot to achieve a speed comparable to the human one while automatically reacting to the giver motion. The approach proposed by Rosenberger et al (2021) is computationally demanding and it is still not suitable for real-time closed-loop control.…”

Section: Introductionmentioning

confidence: 99%

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Handover Control for Human-Robot and Robot-Robot Collaboration

2021

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Modern scenarios in robotics involve human-robot collaboration or robot-robot cooperation in unstructured environments. In human-robot collaboration, the objective is to relieve humans from repetitive and wearing tasks. This is the case of a retail store, where the robot could help a clerk to refill a shelf or an elderly customer to pick an item from an uncomfortable location. In robot-robot cooperation, automated logistics scenarios, such as warehouses, distribution centers and supermarkets, often require repetitive and sequential pick and place tasks that can be executed more efficiently by exchanging objects between robots, provided that they are endowed with object handover ability. Use of a robot for passing objects is justified only if the handover operation is sufficiently intuitive for the involved humans, fluid and natural, with a speed comparable to that typical of a human-human object exchange. The approach proposed in this paper strongly relies on visual and haptic perception combined with suitable algorithms for controlling both robot motion, to allow the robot to adapt to human behavior, and grip force, to ensure a safe handover. The control strategy combines model-based reactive control methods with an event-driven state machine encoding a human-inspired behavior during a handover task, which involves both linear and torsional loads, without requiring explicit learning from human demonstration. Experiments in a supermarket-like environment with humans and robots communicating only through haptic cues demonstrate the relevance of force/tactile feedback in accomplishing handover operations in a collaborative task.

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Human Grasp Classification for Reactive Human-to-Robot Handovers

Cited by 45 publications

References 44 publications

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning

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

Handover Control for Human-Robot and Robot-Robot Collaboration

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