NAV Benchmark

Property:

The control goal is to navigate a robot to a goal region while avoiding an obstacle. Time horizon: t = 6s. Control period: 0.2s.

Initial states:

x1 = [2.9, 3.1]
x2 = [2.9, 3.1]
x3 = [0, 0]
x4 = [0, 0]

Dynamic system: dynamics.m

Goal region ( t=6 ):

x1 = [-0.5, 0.5]
x2 = [-0.5, 0.5]
x3 = [-Inf, Inf]
x4 = [-Inf, Inf]

Obstacle ( always ):

x1 = [1, 2]
x2 = [1, 2]
x3 = [-Inf, Inf]
x4 = [-Inf, Inf]

We provide two networks:

The first network is trained with standard (point-based) reinforcement learning: nn-nav-point.onnx
The second network is trained set-based to improve its verifiable robustness by integrating reachability analysis into the training process: nn-nav-set.onnx