通过高斯近似让智能体避开观测模糊状态，提升导航准确性。
Planning to avoid ambiguous states through Gaussian approximations to non-linear sensors in active inference agents
- 用二阶泰勒展开的高斯近似捕捉传感器非线性带来的曲率信息。
- 该方法在机器人导航中使智能体主动避开难以推断状态的区域。
- 适合研究主动推理与传感器建模的学者参考。
自然界中，主动推理智能体需学习观测如何反映自身状态。工程实践中，传感器物理原理通常已知较准确，测量函数可纳入生成模型。当测量函数为非线性时，常采用高斯分布对变换变量进行近似以保证推断可处理性。本文表明，对测量函数曲率敏感的高斯近似（如二阶泰勒展开）会引入依赖于状态的模糊性项，从而产生对状态的偏好——基于从观测中推断状态的准确性。我们在机器人导航实验中验证了这一偏好效应，展示智能体能据此规划轨迹。
原文摘要 · Abstract (English)
In nature, active inference agents must learn how observations of the world represent the state of the agent. In engineering, the physics behind sensors is often known reasonably accurately and measurement functions can be incorporated into generative models. When a measurement function is non-linear, the transformed variable is typically approximated with a Gaussian distribution to ensure tractable inference. We show that Gaussian approximations that are sensitive to the curvature of the measurement function, such as a second-order Taylor approximation, produce a state-dependent ambiguity term. This induces a preference over states, based on how accurately the state can be inferred from the observation. We demonstrate this preference with a robot navigation experiment where agents plan trajectories.
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