Human motion prediction is an essential step for efficient and safe
human-robot collaboration. Current methods either purely rely on representing
the human joints in some form of neural network-based architecture or use
regression models offline to fit hyper-parameters in the hope of capturing a
model encompassing human motion. While these methods provide good initial
results, they are missing out on leveraging well-studied human body kinematic
models as well as body and scene constraints which can help boost the efficacy
of these prediction frameworks while also explicitly avoiding implausible human
joint configurations. We propose a novel human motion prediction framework that
incorporates human joint constraints and scene constraints in a Gaussian
Process Regression (GPR) model to predict human motion over a set time horizon.
This formulation is combined with an online context-aware constraints model to
leverage task-dependent motions. It is tested on a human arm kinematic model
and implemented on a human-robot collaborative setup with a UR5 robot arm to
demonstrate the real-time capability of our approach. Simulations were also
performed on datasets like HA4M and ANDY. The simulation and experimental
results demonstrate considerable improvements in a Gaussian Process framework
when these constraints are explicitly considered.

人体运动预测是高效和安全的人机协作的重要步骤。我们提出了一种新颖的人体运动预测框架，该框架在高斯过程回归模型中结合了人体关节约束和场景约束，以在一定时间范围内预测人体运动。该框架还结合了在线上下文感知约束模型来利用任务相关的运动，并在人机协作设置中使用 UR5 机器人手臂实现了实时性能。模拟和实验结果表明，当明确考虑这些约束条件时，高斯过程框架的性能得到了显著改善。

利用约束概率人体运动预测增强人机协作

Enhanced Human-Robot Collaboration using Constrained Probabilistic  Human-Motion Prediction

To understand and analyze human behavior, we need to capture humans moving
in, and interacting with, the world. Most existing methods perform 3D human
pose estimation without explicitly considering the scene. We observe however
that the world constrains the body and vice-versa. To motivate this, we show
that current 3D human pose estimation methods produce results that are not
consistent with the 3D scene. Our key contribution is to exploit static 3D
scene structure to better estimate human pose from monocular images. The method
enforces Proximal Relationships with Object eXclusion and is called PROX. To
test this, we collect a new dataset composed of 12 different 3D scenes and RGB
sequences of 20 subjects moving in and interacting with the scenes. We
represent human pose using the 3D human body model SMPL-X and extend SMPLify-X
to estimate body pose using scene constraints. We make use of the 3D scene
information by formulating two main constraints. The inter-penetration
constraint penalizes intersection between the body model and the surrounding 3D
scene. The contact constraint encourages specific parts of the body to be in
contact with scene surfaces if they are close enough in distance and
orientation. For quantitative evaluation we capture a separate dataset with 180
RGB frames in which the ground-truth body pose is estimated using a motion
capture system. We show quantitatively that introducing scene constraints
significantly reduces 3D joint error and vertex error. Our code and data are
available for research at this https URL

该研究通过利用场景结构、建立人体与场景之间的 “临近关系与对象排除法” 来更好地从单眼图像中估计人体姿势，并显示引入场景约束可以显著减少 3D 关节误差和顶点误差。