用双系统融合提升机器人在火星环境下的安全导航能力
Risk-Guided Diffusion: Toward Deploying Robot Foundation Models in Space, Where Failure Is Not An Option
- 结合快速学习与物理仿真双系统，实现自适应与安全性的统一
- 在火星模拟场地实验中故障率降低4倍，且达成与纯学习模型相当的到达率
- 适合对可靠性要求极高的太空探索任务，尤其适用于无训练增补场景
未来机器人太空探索任务需在极端、陌生地形中实现安全可靠的导航。现有生成式AI方法虽能从大规模跨平台数据中学习语义感知的导航策略，但缺乏安全性保障。受人类认知科学启发，我们提出一种风险引导的扩散框架，将快速学习的“系统1”与慢速物理驱动的“系统2”融合，并在训练和推理阶段共享计算，实现适应性与形式化安全的耦合。在NASA JPL的火星模拟场地Mars Yard进行的硬件实验表明，该方法在不增加训练成本的前提下，利用推理时计算资源，使故障率降低最多4倍，同时保持与学习型机器人模型相当的目标到达性能。
原文摘要 · Abstract (English)
Safe, reliable navigation in extreme, unfamiliar terrain is required for future robotic space exploration missions. Recent generative-AI methods learn semantically aware navigation policies from large, cross-embodiment datasets, but offer limited safety guarantees. Inspired by human cognitive science, we propose a risk-guided diffusion framework that fuses a fast, learned "System-1" with a slow, physics-based "System-2", sharing computation at both training and inference to couple adaptability with formal safety. Hardware experiments conducted at the NASA JPL's Mars-analog facility, Mars Yard, show that our approach reduces failure rates by up to $4\times$ while matching the goal-reaching performance of learning-based robotic models by leveraging inference-time compute without any additional training.
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