In tasks where the goal or configuration varies between iterations, human-robot interaction (HRI) can allow the robot to handle repeatable aspects and the human to provide information which adapts to the current state. Advanced interactive robot behaviors are currently realized by inferring human goal or, for physical interaction, adapting robot impedance. While many application-specific heuristics have been proposed for interactive robot behavior, they are often limited in scope, e.g. only considering human ergonomics or task performance. To improve generality, this paper proposes a framework which plans both trajectory and impedance online, handles a mix of task and human objectives, and can be efficiently applied to a new task. This framework can consider many types of uncertainty: contact constraint variation, uncertainty in human goals, or task disturbances. An uncertainty-aware task model is learned from a few demonstrations using Gaussian Processes. This task model is used in a nonlinear model predictive control (MPC) problem to optimize robot trajectory and impedance according to belief in discrete human goals, human kinematics, safety constraints, contact stability, and frequency-domain disturbance rejection. This MPC formulation is introduced, analyzed with respect to convexity, and validated in co-manipulation with multiple goals, a collaborative polishing task, and a collaborative assembly task.
翻译:在目标或配置因迭代而异的任务中,人类机器人互动(HRI)可以让机器人处理重复的方面,而人类则可以提供适合当前状态的信息。高级互动机器人行为目前是通过推断人类目标实现的,或者为了物理互动而实现的。虽然为交互式机器人行为提出了许多针对应用的惯性,但在范围上往往有限,例如,只考虑人类的人体工程学或任务性能。为了改进普遍性,本文件提议了一个框架,既规划轨道,又规划阻碍在线,处理任务和人类目标的混合,并可以有效地应用于新的任务。这个框架可以考虑多种类型的不确定性:接触限制变化、人类目标的不确定性或任务干扰。一个具有不确定性的任务模型是从使用高斯过程的少数演示中学习的。这个任务模型用于非线性模型的预测控制(MPC)问题,以优化机器人的轨迹和阻力,根据对离散的人类目标、人类运动、安全限制、接触稳定性和人类目标的组合,可以有效地应用于新的任务。这个框架可以考虑许多类型的不确定性:接触限制、人类目标的不确定性、人类目标的不确定性、人类目标的不确定性、人类目标的不稳定性或频率的干扰,以及多重任务的校验校正。这个模型将一个对任务目标的研制成。