Rapid advances in perception have enabled large pre-trained models to be used out of the box for transforming high-dimensional, noisy, and partial observations of the world into rich occupancy representations. However, the reliability of these models and consequently their safe integration onto robots remains unknown when deployed in environments unseen during training. In this work, we address this challenge by rigorously quantifying the uncertainty of pre-trained perception systems for object detection via a novel calibration technique based on conformal prediction. Crucially, this procedure guarantees robustness to distribution shifts in states when perceptual outputs are used in conjunction with a planner. As a result, the calibrated perception system can be used in combination with any safe planner to provide an end-to-end statistical assurance on safety in unseen environments. We evaluate the resulting approach, Perceive with Confidence (PwC), in simulation and on hardware where a quadruped robot navigates through previously unseen indoor, static environments. These experiments validate the safety assurances for obstacle avoidance provided by PwC and demonstrate up to $40\%$ improvements in empirical safety compared to baselines.

翻译：感知技术的快速发展使得大型预训练模型能够直接用于将高维、噪声且部分观测的世界信息转化为丰富的占据表征。然而，当这些模型部署于训练时未见过的环境中时，其可靠性及在机器人上的安全集成性仍不明确。本研究通过基于共形预测的新型校准技术，严格量化预训练感知系统在目标检测任务中的不确定性，从而应对这一挑战。关键在于，当感知输出与规划器结合使用时，该程序能保证对状态分布偏移的鲁棒性。因此，校准后的感知系统可与任何安全规划器结合，为未知环境中的安全性提供端到端的统计保障。我们在仿真和硬件实验中评估了所提出的"感知置信度"方法，其中四足机器人在先前未见的静态室内环境中进行导航。这些实验验证了PwC为避障任务提供的安全保障，并证明其相较于基线方法在实证安全性上最高可获得$40\%$的提升。