基于感知动态选择安全节能的步态，提升四足机器人爬楼稳定性
VOCALoco: Viability-Optimized Cost-aware Adaptive Locomotion
- 分模块评估预训练步态的安全性与能耗，实时选优
- 在模拟和真实机器人上实现比端到端强化学习更高的爬楼鲁棒性
- 适合需要安全与能效平衡的复杂地形自主移动场景
近年来，腿式机器人在复杂地形上的运动能力不断提升。然而，多数现有方法依赖端到端深度强化学习（DRL），在安全性与可解释性方面存在局限，尤其在面对新地形时泛化能力不足。为此，我们提出VOCALoco，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。该方法在一组预训练的运动策略中，通过预测执行安全性与固定规划窗口内的预期运输成本，联合评估其可行性和能耗。这一联合评估使系统能够选择在当前局部地形下既安全又节能的策略。我们在楼梯运动任务上评估了该方法，在仿真与真实四足机器人上均验证了其有效性。实验结果表明，相较于传统端到端DRL策略，VOCALoco在上下楼梯过程中展现出更优的鲁棒性与安全性。
原文摘要 · Abstract (English)
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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