Rigorous uncertainty quantification is essential for the safe deployment of autonomous systems in unconstrained environments. Conformal Prediction (CP) provides a distribution-free framework for this task, yet its standard formulations rely on exchangeability assumptions that are violated by the distribution shifts inherent in real-world robotics. Existing online CP methods maintain target coverage by adaptively scaling the conformal threshold, but typically employ a static nonconformity score function. We show that this fixed geometry leads to highly conservative, volume-inefficient prediction regions when environments undergo structural shifts. To address this, we propose $\textbf{AdaptNC}$, a framework for the joint online adaptation of both the nonconformity score parameters and the conformal threshold. AdaptNC leverages an adaptive reweighting scheme to optimize score functions, and introduces a replay buffer mechanism to mitigate the coverage instability that occurs during score transitions. We evaluate AdaptNC on diverse robotic benchmarks involving multi-agent policy changes, environmental changes and sensor degradation. Our results demonstrate that AdaptNC significantly reduces prediction region volume compared to state-of-the-art threshold-only baselines while maintaining target coverage levels.

翻译：摘要：严格的量化不确定性对于自主系统在无约束环境中的安全部署至关重要。共形预测（CP）为此提供了一种无分布框架，但其标准公式依赖于可交换性假设，而真实世界机器人系统中固有的分布偏移违反了这一假设。现有在线CP方法通过自适应缩放共形阈值来维持目标覆盖范围，但通常采用静态的非一致性分数函数。我们证明，当环境发生结构性偏移时，这种固定几何结构会导致高度保守且体积效率低下的预测区域。为解决这一问题，我们提出了$\textbf{AdaptNC}$——一个用于非一致性分数参数和共形阈值联合在线自适应的框架。AdaptNC利用自适应重加权方案优化分数函数，并引入重放缓冲机制以缓解分数转换过程中出现的覆盖不稳定性。我们在涉及多智能体策略变化、环境变化和传感器退化的多种机器人基准上评估了AdaptNC。结果表明，与仅调整阈值的先进基线方法相比，AdaptNC在维持目标覆盖水平的同时显著减小了预测区域体积。