ELLIPSE：基于证据学习的鲁棒路径点和不确定性预测
ELLIPSE: Evidential Learning for Robust Waypoints and Uncertainties
AI总结：
ELLIPSE通过多元深度证据回归方法，提升移动机器人在开放世界中路径点预测的鲁棒性和不确定性估计的可靠性。
摘要：
在开放世界、安全关键环境中，鲁棒路径点预测对移动机器人至关重要。尽管模仿学习（IL）方法在实践中表现出色，但它们容易受到分布偏移的影响：策略可能在不熟悉的状态下变得危险地自信。在本文中，我们提出了ELLIPSE，一种基于多元深度证据回归的方法，能够在单次前向传递中输出路径点和多元学生t分布预测分布。为减少由于视角和姿态扰动接近专家轨迹引起的偏移导致的自信，我们引入了一种轻量级的领域增强程序，该程序在不收集额外演示的情况下合成合理的视角/姿态变化。为了在环境/领域偏移（例如未见台阶）下提高不确定性可靠性，我们应用了后处理等调校，以确保预测集在部署期间保持合理。我们以台阶路径点预测为基础，讨论和实验均围绕获取鲁棒路径点和不确定性展开。广泛的现实世界评估表明，ELLIPSE相比基线方法在任务成功率和不确定性覆盖方面都有所提高。
英文摘要：
Robust waypoint prediction is crucial for mobile robots operating in open-world, safety-critical settings. While Imitation Learning (IL) methods have demonstrated great success in practice, they are susceptible to distribution shifts: the policy can become dangerously overconfident in unfamiliar states. In this paper, we present \textit{ELLIPSE}, a method building on multivariate deep evidential regression to output waypoints and multivariate Student-t predictive distributions in a single forward pass. To reduce covariate-shift-induced overconfidence under viewpoint and pose perturbations near expert trajectories, we introduce a lightweight domain augmentation procedure that synthesizes plausible viewpoint/pose variations without collecting additional demonstrations. To improve uncertainty reliability under environment/domain shift (e.g., unseen staircases), we apply a post-hoc isotonic recalibration on probability integral transform (PIT) values so that prediction sets remain plausible during deployment. We ground the discussion and experiments in staircase waypoint prediction, where obtaining robust waypoint and uncertainty is pivotal. Extensive real world evaluations show that \textit{ELLIPSE} improves both task success rate and uncertainty coverage compared to baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
