Object Handovers: a Review for Robotics

Ortenzi, Valerio; Cosgun, Akansel; Pardi, Tommaso; Chan, Wesley P.; Croft, Elizabeth A.; Kulić, Dana

doi:10.48550/arxiv.2007.12952

Cited by 5 publications

(13 citation statements)

References 167 publications

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“…As the need for collaborative manipulation increases, the importance of researching object handovers between robots and humans continues to grow. A recent survey paper by Ortenzi [11] reviews the progress made in this field. They summarize different capabilities that enable handovers in the robotic system, including communication, grasp planning, perception, and error handling.…”

Section: A Human-to-robot Handoversmentioning

confidence: 99%

“…We measure both objective and subjective metrics. Subjective Metrics: We adopt the metrics described by [11]. We used a subset of questions that were relevant to the study.…”

Section: Dependent Variablesmentioning

confidence: 99%

“…Recent years have seen a proliferation of research in human-robot handovers [1]- [9]. Even though it has been found that communication cues such as speech and gaze positively impact human-robot handovers [10], a recent survey found that a majority of robotic systems that perform handovers did not use any communication cues at all [11]. These findings suggest that there is room for innovation in how to communicate intent for human-robot handovers.…”

Section: Introductionmentioning

confidence: 99%

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Visualizing Robot Intent for Object Handovers with Augmented Reality

Newbury¹,

Cosgun²,

Crowley-Davis³

et al. 2021

Preprint

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Humans are very skillful in communicating their intent for when and where a handover would occur. On the other hand, even the state-of-the-art robotic implementations for handovers display a general lack of communication skills. We propose visualizing the internal state and intent of robots for Human-to-Robot Handovers using Augmented Reality. Specifically, we visualize 3D models of the object and the robotic gripper to communicate the robot's estimation of where the object is and the pose that the robot intends to grasp the object. We conduct a user study with 16 participants, in which each participant handed over a cube-shaped object to the robot 12 times. Results show that visualizing robot intent using augmented reality substantially improves the subjective experience of the users for handovers and decreases the time to transfer the object. Results also indicate that the benefits of augmented reality are still present even when the robot makes errors in localizing the object.

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Section: A Human-to-robot Handoversmentioning

confidence: 99%

“…We measure both objective and subjective metrics. Subjective Metrics: We adopt the metrics described by [11]. We used a subset of questions that were relevant to the study.…”

Section: Dependent Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Visualizing Robot Intent for Object Handovers with Augmented Reality

Newbury¹,

Cosgun²,

Crowley-Davis³

et al. 2021

Preprint

Self Cite

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show abstract

“…Robots can increase efficiency in factories by handing parts and tools to workers, or bring independence to older adults and people with mobility limitations at home by fetching items needed for daily activities. A growing community of robotics researchers have tried to address challenges in enabling seamless autonomous handovers, from human and object perception to motion control and communication during handovers [1].…”

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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“…Many scenarios in which robots assist humans inevitably involve robot-to-human object handovers-the transfer of objects from a robot to a human [1]. Successful handovers are necessary for the completion of tasks across different domains, from fetching medication for older adults in their homes, to handing tools to a worker in a factory.…”

Section: Introductionmentioning

confidence: 99%

Affordance-Aware Handovers with Human Arm Mobility Constraints

Ardón

Cabrera

Pairet

et al. 2020

Preprint

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Reasoning about object handover configurations allows an assistive agent to estimate the appropriateness of handover for a receiver with different arm mobility capacities. While there are existing approaches to estimating the effectiveness of handovers, their findings are limited to users without arm mobility impairments and to specific objects. Therefore, current state-of-the-art approaches are unable to hand over novel objects to receivers with different arm mobility capacities. We propose a method that generalises handover behaviours to previously unseen objects, subject to the constraint of a user's arm mobility levels and the task context. We propose a heuristic-guided hierarchically optimised cost whose optimisation adapts object configurations for receivers with low arm mobility. This also ensures that the robot grasps consider the context of the user's upcoming task, i.e., the usage of the object. To understand preferences over handover configurations, we report on the findings of an online study, wherein we presented different handover methods, including ours, to 259 users with different levels of arm mobility. We encapsulate these preferences in a statistical relational learner (SRL) that is able to reason about the most suitable handover configuration given a receiver's arm mobility and upcoming task. We find that people's preferences over handover methods are correlated to their arm mobility capacities. In experiments with a PR2 robotic platform, we obtained an average handover accuracy of 90.8% when generalising handovers to novel objects. SRL User Study

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Object Handovers: a Review for Robotics

Cited by 5 publications

References 167 publications

Visualizing Robot Intent for Object Handovers with Augmented Reality

Visualizing Robot Intent for Object Handovers with Augmented Reality

Reactive Human-to-Robot Handovers of Arbitrary Objects

Affordance-Aware Handovers with Human Arm Mobility Constraints

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