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。