In this work, we propose a novel strategy to ensure infants, who
inadvertently displace their quilts during sleep, are promptly and accurately
re-covered. Our approach is formulated into two subsequent steps: interference
resolution and quilt spreading. By leveraging the DWPose human skeletal
detection and the Segment Anything instance segmentation models, the proposed
method can accurately recognize the states of the infant and the quilt over
her, which involves addressing the interferences resulted from an infant's
limbs laid on part of the quilt. Building upon prior research, the EM*D deep
learning model is employed to forecast quilt state transitions before and after
quilt spreading actions. To improve the sensitivity of the network in
distinguishing state variation of the handled quilt, we introduce an enhanced
loss function that translates the voxelized quilt state into a more
representative one. Both simulation and real-world experiments validate the
efficacy of our method, in spreading and recover a quilt over an infant.

提出了一种新的策略，通过使用 DWPose 和 Segment Anything 模型来确保无意中移动被子的婴儿能够及时准确地再次覆盖，通过 EM*D 深度学习模型来预测被子状态的转变，并引入一种增强损失函数来提高网络对被子状态变化的敏感度。通过模拟和实际实验验证了该方法的有效性，在婴儿身上平铺和恢复被子。

照料机器人的自主补被技术

Autonomous Quilt Spreading for Caregiving Robots

Whole-body pose estimation localizes the human body, hand, face, and foot
keypoints in an image. This task is challenging due to multi-scale body parts,
fine-grained localization for low-resolution regions, and data scarcity.
Meanwhile, applying a highly efficient and accurate pose estimator to widely
human-centric understanding and generation tasks is urgent. In this work, we
present a two-stage pose \textbf{D}istillation for \textbf{W}hole-body
\textbf{P}ose estimators, named \textbf{DWPose}, to improve their effectiveness
and efficiency. The first-stage distillation designs a weight-decay strategy
while utilizing a teacher's intermediate feature and final logits with both
visible and invisible keypoints to supervise the student from scratch. The
second stage distills the student model itself to further improve performance.
Different from the previous self-knowledge distillation, this stage finetunes
the student's head with only 20% training time as a plug-and-play training
strategy. For data limitations, we explore the UBody dataset that contains
diverse facial expressions and hand gestures for real-life applications.
Comprehensive experiments show the superiority of our proposed simple yet
effective methods. We achieve new state-of-the-art performance on
COCO-WholeBody, significantly boosting the whole-body AP of RTMPose-l from
64.8% to 66.5%, even surpassing RTMPose-x teacher with 65.3% AP. We release a
series of models with different sizes, from tiny to large, for satisfying
various downstream tasks. Our codes and models are available at
this https URL

本研究提出了一种名为 DWPose 的两阶段姿势蒸馏方法，通过权重衰减策略和学生模型自身的蒸馏来提高全身姿势估计的有效性和效率，实验结果表明其在 COCO-WholeBody 数据集上取得了新的最佳性能，将整体 AP 从 64.8% 提升到 66.5%，甚至超过了 RTMPose-x 教师模型的 65.3% AP。