Partially-observable problems pose a trade-off between reducing costs and gathering information. They can be solved optimally by planning in belief space, but that is often prohibitively expensive. Model-predictive control (MPC) takes the alternative approach of using a state estimator to form a belief over the state, and then plan in state space. This ignores potential future observations during planning and, as a result, cannot actively increase or preserve the certainty of its own state estimate. We find a middle-ground between planning in belief space and completely ignoring its dynamics by only reasoning about its future accuracy. Our approach, filter-aware MPC, penalises the loss of information by what we call "trackability", the expected error of the state estimator. We show that model-based simulation allows condensing trackability into a neural network, which allows fast planning. In experiments involving visual navigation, realistic every-day environments and a two-link robot arm, we show that filter-aware MPC vastly improves regular MPC.

翻译：部分可观测问题在降低成本与收集信息之间需要权衡。通过在信念空间中进行规划可以最优地解决此类问题，但这通常计算代价过高。模型预测控制采用另一种方法：使用状态估计器构建关于状态的信念，然后在状态空间中进行规划。这种方法在规划过程中忽略了未来可能的观测信息，因此无法主动提升或维持自身状态估计的确定性。我们在信念空间规划与完全忽略信念动态之间找到一个折中方案——仅推理状态估计的未来精度。我们提出的"基于滤波感知的模型预测控制"方法通过一种称为"可追踪性"的指标来惩罚信息损失，该指标衡量状态估计器的预期误差。研究表明，基于模型的仿真可将可追踪性压缩到神经网络中，从而实现快速规划。在涉及视觉导航、逼真日常环境及两连杆机器人手臂的实验中，我们证明基于滤波感知的模型预测控制显著优于传统模型预测控制。