Despite being trained on increasingly large datasets, robot models often overfit to specific environments or datasets. Consequently, they excel within their training distribution but face challenges in generalizing to novel or unforeseen scenarios. This paper presents a method to proactively identify failure mode probabilities in robot manipulation policies, providing insights into where these models are likely to falter. To this end, since exhaustively searching over a large space of failures is infeasible, we propose a deep reinforcement learning-based framework, RoboFail. It is designed to detect scenarios prone to failure and quantify their likelihood, thus offering a structured approach to anticipate failures. By identifying these high-risk states in advance, RoboFail enables researchers and engineers to better understand the robustness limits of robot policies, contributing to the development of safer and more adaptable robotic systems.

翻译：尽管机器人模型在日益庞大的数据集上进行训练，它们仍常过度拟合特定环境或数据集。因此，这些模型在其训练分布内表现优异，但在泛化至新颖或未预见场景时面临挑战。本文提出一种主动识别机器人操作策略中故障模式概率的方法，从而揭示这些模型可能失效的环节。为此，鉴于在庞大的故障空间中进行穷举搜索不可行，我们提出一个基于深度强化学习的框架——RoboFail。该框架旨在检测易发生故障的场景并量化其发生概率，从而提供一种结构化方法来预测故障。通过预先识别这些高风险状态，RoboFail使研究人员和工程师能更深入地理解机器人策略的鲁棒性极限，为开发更安全、适应性更强的机器人系统作出贡献。