Bodies Uncovered: Learning to Manipulate Real Blankets Around People via Physics Simulations

TL;DR

This paper introduces a physics simulation-based approach for robots to manipulate blankets around people, enabling targeted uncovering of body parts while keeping the rest covered, with successful transfer to real robots.

Contribution

It presents a novel formulation for robotic blanket manipulation and compares reinforcement learning with self-supervised learning approaches in simulation and real-world transfer.

Findings

Simulation-trained policies successfully uncover target body parts.

Reinforcement learning and self-supervised methods both effective.

Real robot experiments demonstrate practical applicability.

Abstract

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.