  Robotic perception models, such as Deep Neural Networks (DNNs), are becoming
more computationally intensive and there are several models being trained with
accuracy and latency trade-offs. However, modern latency accuracy trade-offs
largely report mean accuracy for single-step vision tasks, but there is little
work showing which model to invoke for multi-step control tasks in robotics.
The key challenge in a multi-step decision making is to make use of the right
models at right times to accomplish the given task. That is, the accomplishment
of the task with a minimum control cost and minimum perception time is a
desideratum; this is known as the model selection problem. In this work, we
precisely address this problem of invoking the correct sequence of perception
models for multi-step control. In other words, we provide a provably optimal
solution to the model selection problem by casting it as a multi-objective
optimization problem balancing the control cost and perception time. The key
insight obtained from our solution is how the variance of the perception models
matters (not just the mean accuracy) for multi-step decision making, and to
show how to use diverse perception models as a primitive for energy-efficient
robotics. Further, we demonstrate our approach on a photo-realistic drone
landing simulation using visual navigation in AirSim. Using our proposed
policy, we achieved 38.04% lower control cost with 79.1% less perception time
than other competing benchmarks.
