VOCALoco：生存能力优化的成本感知自适应运动
VOCALoco: Viability-Optimized Cost-aware Adaptive Locomotion
AI总结：
针对足式机器人端到端DRL运动方法安全性与可解释性不足的问题，提出模块化技能选择框架VOCALoco，通过联合评估策略安全性与能耗选择最优策略，在四足机器人楼梯运动任务中表现更优。
摘要：
足式机器人运动领域的最新进展使其能够穿越日益复杂的地形。尽管取得了这些进展，许多现有方法依赖端到端深度强化学习（DRL），这在安全性和可解释性方面存在局限，尤其是在泛化到全新地形时。为克服这些挑战，我们提出了VOCALoco，这是一种模块化的技能选择框架，可根据感知输入动态调整运动策略。给定一组预训练的运动策略，VOCALoco通过在固定规划时域内预测执行安全性和预期运输成本，来评估这些策略的生存能力和能耗。这种联合评估能够在观测到局部地形的情况下，选择既安全又节能的策略。我们在楼梯运动任务上评估了我们的方法，使用四足机器人在仿真和真实场景中验证了其性能。实验结果表明，与传统的端到端DRL策略相比，VOCALoco在上下楼梯过程中实现了更强的鲁棒性和安全性。
英文摘要：
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
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