Effective close-proximity human-robot interaction (CP-HRI) requires robots to be able to both efficiently perform tasks as well as adapt to human behavior and preferences. However, this ability is mediated by many, sometimes competing, aspects of interaction. We propose a real-time motion-planning framework for robotic manipulators that can simultaneously optimize a set of both task- and human-centric cost functions. To this end, we formulate a Nonlinear Model-Predictive Control (NMPC) problem with kino-dynamic constraints and efficiently solve it by leveraging recent advances in nonlinear trajectory optimization. We employ stochastic predictions of the human partner's trajectories in order to adapt the robot's nominal behavior in anticipation of its human partner. Our framework explicitly models and allows balancing of different task- and human-centric cost functions. While previous approaches to trajectory optimization for CP-HRI take anywhere from several seconds to a full minute to compute a trajectory, our approach is capable of computing one in 318 ms on average, enabling real-time implementation. We illustrate the effectiveness of our framework by simultaneously optimizing for separation distance, end-effector visibility, legibility, smoothness, and deviation from nominal behavior. We also demonstrate that our approach performs comparably to prior work in terms of the chosen cost functions, while significantly improving computational efficiency.

翻译：有效的近距离人机交互（CP-HRI）要求机器人既能高效执行任务，又能适应人类行为与偏好。然而，这种能力受到交互过程中多种（有时相互竞争）因素的制约。我们提出一种面向机械臂的实时运动规划框架，能够同时优化一组任务导向和人类导向的成本函数。为此，我们构建了包含运动动力学约束的非线性模型预测控制（NMPC）问题，并利用非线性轨迹优化领域的最新进展高效求解。通过采用对人类伙伴轨迹的随机预测，我们使机器人能预判人类行为并调整其标称行为。该框架显式建模并允许平衡不同的任务导向和人类导向成本函数。以往针对CP-HRI的轨迹优化方法需数秒至数十秒计算轨迹，而我们的方法平均仅需318毫秒即可完成计算，支持实时实现。我们通过同时优化分离距离、末端执行器可见性、可读性、平滑性及与标称行为的偏差，展示了该框架的有效性。实验结果还表明，在所选成本函数方面，本方法与先前工作性能相当，同时计算效率显著提升。