Autonomous robots operating in unstructured, safety-critical environments, from planetary exploration to warehouses and homes, must learn to safely navigate and interact with their surroundings despite limited prior knowledge. Current methods for safe control, such as Hamilton-Jacobi Reachability and Control Barrier Functions, assume known system dynamics. Meanwhile existing safe exploration techniques often fail to account for the unavoidable stochasticity inherent when operating in unknown real world environments, such as an exploratory rover skidding over an unseen surface or a household robot pushing around unmapped objects in a pantry. To address this critical gap, we propose Safe Stochastic Explorer (S.S.Explorer) a novel framework for safe, goal-driven exploration under stochastic dynamics. Our approach strategically balances safety and information gathering to reduce uncertainty about safety in the unknown environment. We employ Gaussian Processes to learn the unknown safety function online, leveraging their predictive uncertainty to guide information-gathering actions and provide probabilistic bounds on safety violations. We first present our method for discrete state space environments and then introduce a scalable relaxation to effectively extend this approach to continuous state spaces. Finally we demonstrate how this framework can be naturally applied to ensure safe physical interaction with multiple unknown objects. Extensive validation in simulation and demonstrative hardware experiments showcase the efficacy of our method, representing a step forward toward enabling reliable widespread robot autonomy in complex, uncertain environments.

翻译：在从行星探索到仓库和家庭等非结构化安全关键环境中运行的自主机器人，必须在先验知识有限的情况下，学会安全导航并与周围环境交互。当前的安全控制方法（如汉密尔顿-雅可比可达性分析与控制屏障函数）通常假设系统动力学已知。而现有的安全探索技术往往未能考虑在未知现实环境中操作时固有的不可避免的随机性，例如探索车在不可见表面打滑，或家用机器人在储藏室推动未建模物体。为填补这一关键空白，我们提出安全随机探索器（S.S.Explorer）——一种在随机动力学下实现安全目标驱动探索的新型框架。该方法通过策略性平衡安全性与信息收集，以降低未知环境中安全性的不确定性。我们采用高斯过程在线学习未知安全函数，利用其预测不确定性来指导信息收集行为，并提供安全违规的概率边界。我们首先展示了该方法在离散状态空间环境中的应用，随后引入可扩展的松弛技术，将该方法有效扩展至连续状态空间。最后，我们演示了该框架如何自然地应用于确保与多个未知物体的安全物理交互。通过大量仿真验证及示范性硬件实验，我们证明了该方法的有效性，为推动机器人在复杂不确定环境中实现可靠的大规模自主性迈出了重要一步。