并非所有错误都生而平等：一种用于检测系统级轨迹预测失败的遗憾度量
Not All Errors Are Made Equal: A Regret Metric for Detecting System-level Trajectory Prediction Failures
AI总结：
针对仅部分预测错误影响下游性能的问题，提出基于遗憾度量的系统级轨迹预测失败检测方法，通过概率泛化兼容不同规划器，并证明利用少量高遗憾数据微调即可高效提升机器人重部署性能。
摘要：
机器人在人类周围运行时，其决策越来越依赖数据驱动的人类预测模型。尽管已知这些模型在分布外交互中会发生预测错误，但只有部分预测错误会影响下游机器人性能。我们提出通过遗憾的数学概念来表征此类“系统级”预测失败：高遗憾交互正是那些预测错误降低了闭环机器人性能的交互。我们进一步引入了遗憾的概率泛化，该校准了不同部署环境下的失败检测，并使遗憾兼容基于奖励和无奖励（如生成式）的规划器。在模拟的自动驾驶交互和部署于硬件的社交导航交互中，我们展示了系统级失败指标可离线用于自动提取闭环人机交互，而这些交互是先前最先进的生成式人类预测器和机器人规划器所难以处理的。我们进一步发现，在人类预测器微调期间，高遗憾数据的存在对机器人重新部署性能的提升具有高度预测性。使用信息量大但显著更小的高遗憾数据（占部署数据的23%）进行微调，与在完整部署数据集上微调具有同等竞争力，这为高效缓解系统级人机交互失败指明了一条有前景的途径。项目网站：https://cmu-intentlab.github.io/not-all-errors/
英文摘要：
Robot decision-making increasingly relies on data-driven human prediction models when operating around people. While these models are known to mispredict in out-of-distribution interactions, only a subset of prediction errors impact downstream robot performance. We propose characterizing such "system-level" prediction failures via the mathematical notion of regret: high-regret interactions are precisely those in which mispredictions degraded closed-loop robot performance. We further introduce a probabilistic generalization of regret that calibrates failure detection across disparate deployment contexts and renders regret compatible with reward-based and reward-free (e.g., generative) planners. In simulated autonomous driving interactions and social navigation interactions deployed on hardware, we showcase that our system-level failure metric can be used offline to automatically extract closed-loop human-robot interactions that state-of-the-art generative human predictors and robot planners previously struggled with. We further find that the very presence of high-regret data during human predictor fine-tuning is highly predictive of robot re-deployment performance improvements. Fine-tuning with the informative but significantly smaller high-regret data (23% of deployment data) is competitive with fine-tuning on the full deployment dataset, indicating a promising avenue for efficiently mitigating system-level human-robot interaction failures. Project website: https://cmu-intentlab.github.io/not-all-errors/
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。
