Affordance-Aware Handovers With Human Arm Mobility Constraints

Ardón, Paola; Cabrera, María Eugenia; Pairet, Èric; Petrick, Ronald P. A.; Ramamoorthy, Subramanian; Lohan, Katrin Solveig; Çakmak, Maya

doi:10.1109/lra.2021.3062808

Cited by 18 publications

(6 citation statements)

References 27 publications

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“…Multi-agent approaches have also been explored [11], some of which utilize game theory models [12], while others use a "neglect"-based model [8,13]. Task-oriented approaches include [14,15], which focus on grasping and handovers, respectively, or [16,17] which focus on task scheduling and assessment within a human-specified task. These systems could likely be adapted to a broader array of problems than their authors have presented, but this remains unexplored.…”

Section: Background 21 Robot Self-assessmentmentioning

confidence: 99%

Toward Competent Robot Apprentices: Enabling Proactive Troubleshooting in Collaborative Robots

Thierauf,

Law,

Frasca

et al. 2024

Machines

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For robots to become effective apprentices and collaborators, they must exhibit some level of autonomy, for example, recognizing failures and identifying ways to address them with the aid of their human teammates. In this systems paper, we present an integrated cognitive robotic architecture for a “robot apprentice” that is capable of assessing its own performance, identifying task execution failures, communicating them to humans, and resolving them, if possible. We demonstrate the capabilities of our proposed architecture with a series of demonstrations and confirm with an online user study that people prefer our robot apprentice compared to robots without those capabilities.

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Section: Background 21 Robot Self-assessmentmentioning

confidence: 99%

Toward Competent Robot Apprentices: Enabling Proactive Troubleshooting in Collaborative Robots

Thierauf,

Law,

Frasca

et al. 2024

Machines

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“…Ardon et al [20] proposed a method for computing appropriate handover location and robot configuration for people with limited arm mobility. By doing so, they addressed the handover orientation problem implicitly.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the affordance-aware rule-based method was also implemented. This method was inspired by the work of Ardon et al [20], but instead of optimizing for their proposed criteria, we simplify the computational part of the handover orientation into a single if statement depending on the detected affordances.…”

Section: A Computing Object Handover Orientationsmentioning

confidence: 99%

Optimizing Robot-to-Human Object Handovers using Vision-based Affordance Information

Lehotsky,

Christensen,

Chrysostomou

2023

2023 IEEE International Conference on Imaging Systems and Techniques (IST)

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Robotic handovers of objects to humans require selecting appropriate grasp poses and orientations to enable efficient subsequent use. We present two methods to compute suitable handover orientations based solely on object affordances rather than object categories or predefined object-specific rules. The first uses human demonstration data to learn average handover orientations per object directly from affordances. The second is a rule-based method that orients graspable affordances towards the receiver. We integrated both approaches into a robotic system performing task-oriented grasping and handovers based on affordance segmentation. A user study indicates the rule-based method produces equally comfortable and natural handover orientations compared to learning from demonstration, while being simpler to implement. Further experiments demonstrate the robot's ability to successfully hand over objects with proper orientations. This is the first prototype deriving handover orientations solely from affordances treated as pixelwise semantic segmentation, providing a practical approach without per-object datasets. https://bit.ly/RobotHandovers

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“…There is a signiőcant amount of literature on human-robot object handover with the potential to enhance robot capabilities performed from a range of aspects, such as visualising robot intent [10], adaptiveness to user preference [11], visual perception for handover [12], affordance-based handover [13], and gripper effort control [7]. These approaches are based on speciőc aspects, such as the human hands, objects, contact points, and contact pressure.…”

Section: Related Workmentioning

confidence: 99%

Towards Smooth Human-Robot Handover with a Vision-Based Tactile Sensor

Rayamane,

Munguia-Galeano,

Tafrishi

et al. 2023

Lecture Notes in Computer Science

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Cooperative human-robot interaction often requires successful handovers of objects between the two entities. However, the assumption that a human can reliably grasp an object from a robot is not always valid. To address this issue, we propose a vision-based tactile sensor for object handover framework that utilises a low-cost sensor with variable sensitivity and pressure. The sensor comprises a latex layer that makes contact with the object and a tracking marker that registers the resulting changes in position. By pre-processing this information, a robot can determine whether it is necessary to open the gripper. Our approach is validated through an exploratory user study involving ten participants who completed handover tasks involving eight objects of varying shapes and stiffness, including rigid and deformable objects like raspberries and dough. The study results demonstrate the effectiveness of our approach, with a success rate of 94%. Additionally, users reported less difficulty performing the handover tasks when the sensitivity value was decreased. Overall, our vision-based tactile sensor framework offers a promising solution for the challenging problem of human-robot handover in cooperative settings.

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Affordance-Aware Handovers With Human Arm Mobility Constraints

Cited by 18 publications

References 27 publications

Toward Competent Robot Apprentices: Enabling Proactive Troubleshooting in Collaborative Robots

Toward Competent Robot Apprentices: Enabling Proactive Troubleshooting in Collaborative Robots

Optimizing Robot-to-Human Object Handovers using Vision-based Affordance Information

Towards Smooth Human-Robot Handover with a Vision-Based Tactile Sensor

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