Humanoid robots are expected to navigate in changing environments and perform a variety of tasks. Frequently, these tasks require the robot to make decisions online regarding the speed and precision of following a reference path. For example, a robot may want to decide to temporarily deviate from its path to overtake a slowly moving obstacle that shares the same path and is ahead. In this case, path following performance is compromised in favor of fast path traversal. Available global trajectory tracking approaches typically assume a given -- specified in advance -- time parametrization of the path and seek to minimize the norm of the Cartesian error. As a result, when the robot should be where on the path is fixed and temporary deviations from the path are strongly discouraged. Given a global path, this paper presents a Model Predictive Contouring Control (MPCC) approach to selecting footsteps that maximize path traversal while simultaneously allowing the robot to decide between faithful versus fast path following. The method is evaluated in high-fidelity simulations of the bipedal robot Digit in terms of tracking performance of curved paths under disturbances and is also applied to the case where Digit overtakes a moving obstacle.

翻译：人形机器人需在动态环境中导航并执行多样化任务。这些任务常要求机器人实时决策，平衡路径跟随速度与精度。例如，当机器人与缓慢移动的障碍物共享同一路径时，可能需要暂时偏离既定路径以完成超越。在此场景下，为追求快速路径穿越而牺牲路径跟随性能。现有全局轨迹跟踪方法通常预设路径的时间参数化，并通过最小化笛卡尔误差范数进行优化，导致机器人在路径上的时空位置被固定，临时偏离受到严格限制。本文基于全局路径提出一种模型预测轮廓控制（MPCC）方法，在生成步态规划时最大化路径穿越效率，同时允许机器人自主选择精确跟随与快速跟随模式。通过双足机器人Digit的高保真仿真评估该方法在曲线路径扰动下的轨迹跟踪性能，并成功应用于Digit超越移动障碍物的场景。