Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Aggressive Perception-Aware Navigation using Deep Optical Flow Dynamics
  and PixelMPC   Recently, vision-based control has gained traction by leveraging the power of
machine learning. In this work, we couple a model predictive control (MPC)
framework to a visual pipeline. We introduce deep optical flow (DOF) dynamics,
which is a combination of optical flow and robot dynamics. Using the DOF
dynamics, MPC explicitly incorporates the predicted movement of relevant pixels
into the planned trajectory of a robot. Our implementation of DOF is
memory-efficient, data-efficient, and computationally cheap so that it can be
computed in real-time for use in an MPC framework. The suggested Pixel Model
Predictive Control (PixelMPC) algorithm controls the robot to accomplish a
high-speed racing task while maintaining visibility of the important features
(gates). This improves the reliability of vision-based estimators for
localization and can eventually lead to safe autonomous flight. The proposed
algorithm is tested in a photorealistic simulation with a high-speed drone
racing task.

7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn