Recent advancements in legged robot locomotion have facilitated traversal over increasingly complex terrains. Despite this progress, many existing approaches rely on end-to-end deep reinforcement learning (DRL), which poses limitations in terms of safety and interpretability, especially when generalizing to novel terrains. To overcome these challenges, we introduce VOCALoco, a modular skill-selection framework that dynamically adapts locomotion strategies based on perceptual input. Given a set of pre-trained locomotion policies, VOCALoco evaluates their viability and energy-consumption by predicting both the safety of execution and the anticipated cost of transport over a fixed planning horizon. This joint assessment enables the selection of policies that are both safe and energy-efficient, given the observed local terrain. We evaluate our approach on staircase locomotion tasks, demonstrating its performance in both simulated and real-world scenarios using a quadrupedal robot. Empirical results show that VOCALoco achieves improved robustness and safety during stair ascent and descent compared to a conventional end-to-end DRL policy

翻译：近年来，腿式机器人的步态控制技术已实现在日益复杂地形上的稳定行进。尽管取得了这些进展，现有方法大多依赖端到端的深度强化学习（DRL），其在安全性和可解释性方面存在局限，特别是在泛化至新地形时。为应对这些挑战，我们提出了VOCALoco——一种基于感知输入动态调整步态策略的模块化技能选择框架。给定一组预训练的步态控制策略，VOCALoco通过预测固定规划时域内的执行安全性与预期运输能耗，综合评估各策略的生存可行性与能量消耗。这种联合评估机制能够根据观测到的局部地形特征，选择兼具安全性与能效最优的策略。我们在楼梯行进任务中对该方法进行了验证，通过四足机器人的仿真与实体实验证明了其有效性。实证结果表明，相较于传统端到端DRL策略，VOCALoco在楼梯攀爬与下降任务中实现了更高的鲁棒性与安全性。