Perceive With Confidence: Statistical Safety Assurances for Navigation with Learning-Based Perception
arxiv(2024)
摘要
Rapid advances in perception have enabled large pre-trained models to be used
out of the box for processing high-dimensional, noisy, and partial observations
of the world into rich geometric representations (e.g., occupancy predictions).
However, safe integration of these models onto robots remains challenging due
to a lack of reliable performance in unfamiliar environments. In this work, we
present a framework for rigorously quantifying the uncertainty of pre-trained
perception models for occupancy prediction in order to provide end-to-end
statistical safety assurances for navigation. We build on techniques from
conformal prediction for producing a calibrated perception system that lightly
processes the outputs of a pre-trained model while ensuring generalization to
novel environments and robustness to distribution shifts in states when
perceptual outputs are used in conjunction with a planner. The calibrated
system can be used in combination with any safe planner to provide an
end-to-end statistical assurance on safety in a new environment with a
user-specified threshold 1-ϵ. We evaluate the resulting approach -
which we refer to as Perceive with Confidence (PwC) - with experiments in
simulation and on hardware where a quadruped robot navigates through indoor
environments containing objects unseen during training or calibration. These
experiments validate the safety assurances provided by PwC and demonstrate
significant improvements in empirical safety rates compared to baselines.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要