TRIAGE: 通过概率-epistemic门控估计进行类型路由干预 在机器人操作和自适应感知中 -- 不要将所有不确定性视为相同
TRIAGE: Type-Routed Interventions via Aleatoric-Epistemic Gated Estimation in Robotic Manipulation and Adaptive Perception -- Don't Treat All Uncertainty the Same
- University of Illinois at Chicago（伊利诺伊大学香槟分校）
- Intel Labs（英特尔实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
TRIAGE通过分解不确定性为概率和epistemic组件，改进机器人操作和自适应感知的性能和效率。
AI中文摘要：
大多数关注不确定性的机器人系统将预测不确定性聚合为一个标量分数，并使用它来触发统一的纠正响应。这种聚合会掩盖不确定性是否源于受损的观测或学习模型与真实系统动态之间的不匹配。因此，纠正措施可能应用于闭环的错误部分，导致性能下降，相对于保持策略不变。我们介绍了一个轻量级的后置框架，将不确定性分解为概率和epistemic组件，并利用这些信号在推理时间调节系统响应。概率不确定性是从观测分布中的偏差使用Mahalanobis密度模型进行估计，而epistemic不确定性通过噪声稳健的前向动态集合来检测，该集合将模型不匹配与测量损坏分离。这两种信号在闭环执行期间经验上几乎正交，并允许特定类型的响应。高概率不确定性触发观测恢复，而高epistemic不确定性调节控制动作。相同的信号还通过在跟踪推理期间指导模型容量选择来调节自适应感知。实验显示在控制和感知任务中均有一致的改进。在机器人操作中，分解的控制器在复合扰动下将任务成功率从59.4%提高到80.4%，并优于结合不确定性基线的最高21.0%。在MOT17上的自适应跟踪推理中，不确定性引导的模型选择将平均计算量减少58.2%，相对于固定高容量检测器，同时保持检测质量在0.4%以内。代码和演示视频可在https://divake.github.io/uncertainty-decomposition/上获得。
英文摘要：
Most uncertainty-aware robotic systems collapse prediction uncertainty into a single scalar score and use it to trigger uniform corrective responses. This aggregation obscures whether uncertainty arises from corrupted observations or from mismatch between the learned model and the true system dynamics. As a result, corrective actions may be applied to the wrong component of the closed loop, degrading performance relative to leaving the policy unchanged. We introduce a lightweight post hoc framework that decomposes uncertainty into aleatoric and epistemic components and uses these signals to regulate system responses at inference time. Aleatoric uncertainty is estimated from deviations in the observation distribution using a Mahalanobis density model, while epistemic uncertainty is detected using a noise robust forward dynamics ensemble that isolates model mismatch from measurement corruption. The two signals remain empirically near orthogonal during closed loop execution and enable type specific responses. High aleatoric uncertainty triggers observation recovery, while high epistemic uncertainty moderates control actions. The same signals also regulate adaptive perception by guiding model capacity selection during tracking inference. Experiments demonstrate consistent improvements across both control and perception tasks. In robotic manipulation, the decomposed controller improves task success from 59.4% to 80.4% under compound perturbations and outperforms a combined uncertainty baseline by up to 21.0%. In adaptive tracking inference on MOT17, uncertainty-guided model selection reduces average compute by 58.2% relative to a fixed high capacity detector while preserving detection quality within 0.4%. Code and demo videos are available at https://divake.github.io/uncertainty-decomposition/.
