Affordance-Aware Handovers with Human Arm Mobility Constraints

Autor: Paola Ardón, Katrin Solveig Lohan, Maya Cakmak, Èric Pairet, Subramanian Ramamoorthy, Ronald P. A. Petrick, Maria Eugenia Cabrera
Jazyk: angličtina
Rok vydání: 2020
Předmět:
FOS: Computer and information sciences
0209 industrial biotechnology
Control and Optimization
Computer science
grasping
Biomedical Engineering
Context (language use)
02 engineering and technology
Human–robot interaction
Task (project management)
human-robot interaction
Computer Science - Robotics
020901 industrial engineering & automation
humanoids
Artificial Intelligence
Human–computer interaction
0202 electrical engineering
electronic engineering
information engineering
Affordance
Mechanical Engineering
ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS
Object (computer science)
Computer Science Applications
Human-Computer Interaction
Handover
Control and Systems Engineering
Task analysis
Robot
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Robotics (cs.RO)
Zdroj: Ardón, P, Cabrera, M E, Pairet, È, Petrick, R P A, Ramamoorthy, S, Lohan, K S & Cakmak, M 2021, ' Affordance-Aware Handovers With Human Arm Mobility Constraints ', IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 3136-3143 . https://doi.org/10.1109/LRA.2021.3062808
DOI: 10.1109/LRA.2021.3062808
Popis: 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 for 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 find that people's preferences over handover methods are correlated to their arm mobility capacities. We encapsulate these preferences in a statistical relational model (SRL) that is able to reason about the most suitable handover configuration given a receiver's arm mobility and upcoming task. Using our SRL model, we obtained an average handover accuracy of $90.8\%$ when generalising handovers to novel objects.
Accepted for RA-L 2021
Databáze: OpenAIRE
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