PIETRA：用于穿越分布外地形的物理感知证据学习
PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain
AI总结：
提出PIETRA框架，通过将物理先验融入证据神经网络及物理感知损失，实现分布外输入下学习与物理预测的无缝切换，提升越野导航准确性与性能。
摘要：
自监督学习是开发越野导航可行驶性模型的强大方法，但这些模型在处理训练期间未见的输入时常常遇到困难。现有方法利用证据深度学习等技术来量化模型不确定性，从而帮助识别并避开分布外地地形。然而，总是避开分布外地形可能过于保守，例如当可以使用基于物理的模型有效地分析新地形时。为了克服这一挑战，我们提出了物理感知证据可行驶性（PIETRA），这是一个自监督学习框架，它将物理先验直接整合到证据神经网络的数学公式中，并通过不确定性感知的物理感知训练损失隐式引入物理知识。我们的证据网络在分布外输入下能够在学习预测和基于物理的预测之间无缝切换。此外，物理感知损失对学习模型进行了正则化，确保与物理模型更好地对齐。大量仿真和硬件实验表明，PIETRA在存在显著分布偏移的环境中提高了学习准确性和导航性能。
英文摘要：
Self-supervised learning is a powerful approach for developing traversability models for off-road navigation, but these models often struggle with inputs unseen during training. Existing methods utilize techniques like evidential deep learning to quantify model uncertainty, helping to identify and avoid out-of-distribution terrain. However, always avoiding out-of-distribution terrain can be overly conservative, e.g., when novel terrain can be effectively analyzed using a physics-based model. To overcome this challenge, we introduce Physics-Informed Evidential Traversability (PIETRA), a self-supervised learning framework that integrates physics priors directly into the mathematical formulation of evidential neural networks and introduces physics knowledge implicitly through an uncertainty-aware, physics-informed training loss. Our evidential network seamlessly transitions between learned and physics-based predictions for out-of-distribution inputs. Additionally, the physics-informed loss regularizes the learned model, ensuring better alignment with the physics model. Extensive simulations and hardware experiments demonstrate that PIETRA improves both learning accuracy and navigation performance in environments with significant distribution shifts.
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。
