Ensuring Safe Navigation with Statistical Confidence

Основні поняття

The author presents a framework for quantifying uncertainty in pre-trained perception models to ensure safe navigation, providing end-to-end statistical safety assurances. By calibrating the perception system and incorporating a non-deterministic filter, the approach guarantees safety in new environments.

Анотація

The content discusses the challenges of integrating pre-trained perception models into robotic systems for safe navigation. It introduces a framework called Perceive with Confidence (PWC) that uses conformal prediction to provide statistical safety assurances. The method is validated through simulations and hardware experiments on a quadruped robot, showcasing significant improvements in safety rates compared to baselines.

Rapid advances in perception have enabled large pre-trained models to process high-dimensional observations.
Safe integration onto robots remains challenging due to unreliable performance in unfamiliar environments.
PWC framework quantifies uncertainty for occupancy prediction, ensuring end-to-end statistical safety assurances.
Calibration procedure accounts for closed-loop distribution shifts and provides reliable outputs for safe planning.
Hardware validation demonstrates PWC's effectiveness in real-world environments with low misdetection rates and high success rates.

Статистика

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.

Цитати

"The calibrated system can be used in combination with any safe planner to provide an end-to-end statistical assurance on safety."
"Our goal is to provide a statistical assurance on safety for the end-to-end policy πϕ."

Ключові висновки, отримані з

Perceive With Confidence

by Anushri Dixi... о arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.08185.pdf

Глибші Запити

How can the concept of conformal prediction be applied beyond robotics

Conformal prediction, a framework for uncertainty quantification, can be applied beyond robotics in various fields such as healthcare, finance, and climate science. In healthcare, conformal prediction can be used to provide statistical assurances on the predictions made by medical models or diagnostic tools. For example, in personalized medicine, conformal prediction can help determine the reliability of treatment recommendations based on patient data. In finance, it can be utilized to assess the risk associated with investment decisions or predict market trends with confidence intervals. Climate science could benefit from conformal prediction by providing reliable estimates of future climate scenarios and their uncertainties.

What are potential limitations of relying solely on pre-trained perception models for navigation

Relying solely on pre-trained perception models for navigation poses several limitations. One major limitation is the lack of adaptability to new environments or unforeseen obstacles. Pre-trained models may not generalize well to novel situations that were not present in the training data set, leading to potential errors in obstacle detection or path planning. Additionally, these models may not account for real-time changes in the environment such as moving objects or dynamic obstacles which could impact navigation safety. Furthermore, pre-trained models might have inherent biases or limitations that could affect their performance in certain scenarios.

How might advancements in sensor technology impact the efficacy of calibration procedures like PWC

Advancements in sensor technology can significantly impact the efficacy of calibration procedures like PWC (Perceive with Confidence). Improved sensors with higher resolution and accuracy can provide more detailed and precise observations of the environment to perception systems. This enhanced sensory input can lead to better calibration outcomes by reducing uncertainty and improving object detection accuracy. Additionally, sensors with advanced features like multi-modal capabilities (e.g., combining RGB-D imaging with LiDAR) can offer richer information for calibration procedures to leverage when estimating uncertainties and making safety assessments during navigation tasks.