def create_params():
p = DotMap()
# Load the dependencies
p.system_dynamics_params = create_system_dynamics_params()
p.waypoint_params = create_waypoint_params()
# p.pipeline = ControlPipelineV0
# New control pipeline v1 doesn't rescale the trajectory to maximize the speed
p.pipeline = ControlPipelineV1
# The directory for saving the control pipeline files
# p.dir = '/home/anjianl/Desktop/project/WayPtNav/data/control_data/control_pipelines'
# New control pipeline v1 doesn't rescale the trajectory to maximize the speed
p.dir = '/home/anjianl/Desktop/project/WayPtNav/data/control_data/new_control_pipelines'
# Spline parameters
p.spline_params = DotMap(spline=Spline3rdOrder,
max_final_time=6.0,
epsilon=1e-5)
# If planning a trajectory of 3s, it will be very greedy and oscillate a lot.
# p.spline_params = DotMap(spline=Spline3rdOrder,
# max_final_time=3.0,
# epsilon=1e-5)
p.minimum_spline_horizon = 1.5
# LQR setting parameters
p.lqr_params = DotMap(cost_fn=QuadraticRegulatorRef,
quad_coeffs=np.array([1.0, 1.0, 1.0, 1.0, 1.0],
dtype=np.float32),
linear_coeffs=np.zeros((5), dtype=np.float32))
# Velocity binning parameters
p.binning_parameters = DotMap(
num_bins=61,
min_speed=p.system_dynamics_params.v_bounds[0],
max_speed=p.system_dynamics_params.v_bounds[1])
# Converting K to world coordinates is slow
# so only set this to true when LQR data is needed
p.convert_K_to_world_coordinates = False
# When not needed, LQR controllers can be discarded
# to save memory
p.discard_LQR_controller_data = True
# Set this to True to ignore precomputed
# LQR trajectories
p.discard_precomputed_lqr_trajectories = False
# Set this to true if you want trajectory objects to track
# linear and angular acceleration. If not set to false to save memory
p.track_trajectory_acceleration = True
p.verbose = False
return p
def create_rgb_trainer_params():
from params.simulator.sbpd_simulator_params import create_params as create_simulator_params
from params.visual_navigation_trainer_params import create_params as create_trainer_params
from params.waypoint_grid.sbpd_image_space_grid import create_params as create_waypoint_params
from params.model.resnet50_arch_v1_params import create_params as create_model_params
from params.reachability_map.reachability_map_params import create_params as create_reachability_map_params
# Load the dependencies
simulator_params = create_simulator_params()
# Seed
simulator_params.seed = 10
# Ensure the waypoint grid is projected SBPD Grid
simulator_params.planner_params.control_pipeline_params.waypoint_params = create_waypoint_params(
)
# Ensure the renderer modality is rgb
simulator_params.obstacle_map_params.renderer_params.camera_params.modalities = [
'rgb'
]
simulator_params.obstacle_map_params.renderer_params.camera_params.img_channels = 3
simulator_params.obstacle_map_params.renderer_params.camera_params.width = 1024
simulator_params.obstacle_map_params.renderer_params.camera_params.height = 1024
simulator_params.obstacle_map_params.renderer_params.camera_params.im_resize = 0.21875
# Change episode horizon
simulator_params.episode_horizon_s = 80.0
# Ensure the renderer is using area3
# TODO: When generating our own data, choose area 3, area4, area5
# When testing, choosing area 1
simulator_params.obstacle_map_params.renderer_params.building_name = 'area4'
# TODO: area3: thread='v1'; area4: thread='v2'; area5a: thread='v3'
simulator_params.reachability_map_params.thread = 'v2'
# specify reachability data dir name according to building_name and thread
create_reachability_data_dir_params(
simulator_params.reachability_map_params)
# Save trajectory data
simulator_params.save_trajectory_data = True
# Choose cost function
# TODO: in training, always use 'heuristics'
# simulator_params.cost = 'heuristics' # heuristics cost.
simulator_params.cost = 'reachability'
p = create_trainer_params(simulator_params=simulator_params)
# Create the model params
p.model = create_model_params()
return p
def create_params():
p = DotMap()
# Load the dependencies
p.system_dynamics_params = create_system_dynamics_params()
p.waypoint_params = create_waypoint_params()
p.pipeline = ControlPipelineV0
# The directory for saving the control pipeline files
p.dir = os.path.join(base_data_dir(), 'control_pipelines')
# Spline parameters
p.spline_params = DotMap(
spline=Spline3rdOrder,
max_final_time=10.0, #60 crashes pc with 32Gb ram, 6 is default
epsilon=1e-5)
p.minimum_spline_horizon = 1.5 # default 1.5
# LQR setting parameters
p.lqr_params = DotMap(cost_fn=QuadraticRegulatorRef,
quad_coeffs=np.array([1.0, 1.0, 1.0, 1.0, 1.0],
dtype=np.float32),
linear_coeffs=np.zeros((5), dtype=np.float32))
# Velocity binning parameters
p.binning_parameters = DotMap(
num_bins=20, # 61 crashes pc with 32gb ram, 15 is slow
min_speed=p.system_dynamics_params.v_bounds[0],
max_speed=p.system_dynamics_params.v_bounds[1])
# Converting K to world coordinates is slow
# so only set this to true when LQR data is needed
p.convert_K_to_world_coordinates = False
# When not needed, LQR controllers can be discarded
# to save memory
p.discard_LQR_controller_data = True
# Set this to True to ignore precomputed
# LQR trajectories
p.discard_precomputed_lqr_trajectories = False
# Set this to true if you want trajectory objects to track
# linear and angular acceleration. If not set to false to save memory
p.track_trajectory_acceleration = True
p.verbose = True
return p
def create_rgb_trainer_params():
from params.simulator.sbpd_simulator_params import create_params as create_simulator_params
from params.visual_navigation_trainer_params import create_params as create_trainer_params
from params.waypoint_grid.sbpd_image_space_grid import create_params as create_waypoint_params
from params.model.resnet50_arch_v1_params import create_params as create_model_params
# Load the dependencies
simulator_params = create_simulator_params()
# Ensure the waypoint grid is projected SBPD Grid
simulator_params.planner_params.control_pipeline_params.waypoint_params = create_waypoint_params(
)
# Ensure the renderer modality is rgb
simulator_params.obstacle_map_params.renderer_params.camera_params.modalities = [
'rgb'
]
simulator_params.obstacle_map_params.renderer_params.camera_params.img_channels = 3
simulator_params.obstacle_map_params.renderer_params.camera_params.width = 1024
simulator_params.obstacle_map_params.renderer_params.camera_params.height = 1024
simulator_params.obstacle_map_params.renderer_params.camera_params.im_resize = 0.21875
# Ensure the renderer is using area1
simulator_params.obstacle_map_params.renderer_params.building_name = 'area1'
# Change the episode horizon
simulator_params.episode_horizon_s = 80.0
# Save trajectory data
simulator_params.save_trajectory_data = True
p = create_trainer_params(simulator_params=simulator_params)
# Create the model params
p.model = create_model_params()
return p