Bodies Uncovered: Learning to Manipulate Real Blankets Around People via Physics Simulations
| Autor: | Zackory Erickson, Kavya Puthuveetil, Charles Kemp |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Human-Computer Interaction Computer Science - Robotics Control and Optimization Artificial Intelligence Control and Systems Engineering Mechanical Engineering Biomedical Engineering Computer Vision and Pattern Recognition Robotics (cs.RO) Computer Science Applications |
| Zdroj: | IEEE Robotics and Automation Letters. 7:1984-1991 |
| ISSN: | 2377-3774 |
| DOI: | 10.1109/lra.2022.3142732 |
| Popis: | While robots present an opportunity to provide physical assistance to older adults and people with mobility impairments in bed, people frequently rest in bed with blankets that cover the majority of their body. To provide assistance for many daily self-care tasks, such as bathing, dressing, or ambulating, a caregiver must first uncover blankets from part of a person's body. In this work, we introduce a formulation for robotic bedding manipulation around people in which a robot uncovers a blanket from a target body part while ensuring the rest of the human body remains covered. We compare two approaches for optimizing policies which provide a robot with grasp and release points that uncover a target part of the body: 1) reinforcement learning and 2) self-supervised learning with optimization to generate training data. We trained and conducted evaluations of these policies in physics simulation environments that consist of a deformable cloth mesh covering a simulated human lying supine on a bed. In addition, we transfer simulation-trained policies to a real mobile manipulator and demonstrate that it can uncover a blanket from target body parts of a manikin lying in bed. Source code is available online. Comment: to be published in IEEE Robotics and Automation Letters, 8 pages, 9 figures, 2 tables |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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