A new control paradigm using angular momentum and foot placement as state variables in the linear inverted pendulum model has expanded the realm of possibilities for the control of bipedal robots. This new paradigm, known as the ALIP model, has shown effectiveness in cases where a robot's center of mass height can be assumed to be constant or near constant as well as in cases where there are no non-kinematic restrictions on foot placement. Walking up and down stairs violates both of these assumptions, where center of mass height varies significantly within a step and the geometry of the stairs restrict the effectiveness of foot placement. In this paper, we explore a variation of the ALIP model that allows the length of the virtual pendulum formed by the robot's stance foot and center of mass to follow smooth trajectories during a step. We couple this model with a control strategy constructed from a novel combination of virtual constraint-based control and a model predictive control algorithm to stabilize a stair climbing gait that does not soley rely on foot placement. Simulations on a 20-degree of freedom model of the Cassie biped in the SimMechanics simulation environment show that the controller is able to achieve periodic gait.

翻译：采用角动量与落脚点作为状态变量的线性倒立摆模型新控制范式，拓展了双足机器人控制的可能性。该新范式被称为ALIP模型，在机器人质心高度可视为恒定或近似恒定，以及落脚点不受非运动学约束的条件下均展现出有效性。然而上下楼梯过程同时违背这两项假设：单步内质心高度显著变化，且楼梯几何结构限制了落脚点的有效性。本文探索ALIP模型的变体，允许机器人支撑脚与质心构成的虚拟摆长在单步内沿平滑轨迹变化。我们将该模型与基于虚拟约束控制及模型预测控制算法的新型联合控制策略相结合，以稳定不单纯依赖落脚点的楼梯攀爬步态。在SimMechanics仿真环境下对Cassie双足机器人20自由度模型的仿真表明，该控制器能够实现周期性步态。