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.

翻译：人体运动预测是实现高效安全人机协作的关键步骤。当前方法要么纯粹依赖某种神经网络架构表示人体关节点，要么使用离线回归模型拟合超参数以期捕捉涵盖人体运动的通用模型。尽管这些方法能取得初步良好效果，但它们未能充分利用经过充分验证的人体运动学模型以及身体与场景约束——这些约束既能提升预测框架效能，又能明确避免不合情理的人体关节构型。我们提出一种新型人体运动预测框架，该框架在高斯过程回归（GPR）模型中融合人体关节约束与场景约束，以预测设定时间范围内的人体运动。该模型与在线情境感知约束模型相结合，可有效利用任务依赖型运动模式。我们基于人体手臂运动学模型进行测试，并在配备UR5机械臂的人机协作实验平台上验证了方法的实时性能。同时在HA4M和ANDY等数据集上开展仿真实验。仿真与实验结果表明，当显式纳入这些约束时，高斯过程框架的性能获得了显著提升。