Dynamic Selection of Perception Models for Robotic Control

TL;DR

This paper introduces an optimal method for selecting perception models in multi-step robotic control tasks, balancing control cost and perception latency, and demonstrates significant efficiency improvements in drone landing simulations.

Contribution

It formulates the model selection as a multi-objective optimization problem and highlights the importance of perception model variance in multi-step decision making.

Findings

Achieved 38.04% lower control cost

Reduced perception time by 79.1%

Validated on a drone landing simulation

Abstract

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.