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.

本文采用滤波器感知的模型预测控制方法，既考虑了不确定性的影响，又可以通过建立一个包含感知能力的模型来显著提高控制性能。