def __init__(self):
Genotype.__init__(self, orig_size_xyz=IND_SIZE)
self.add_network(CPPN(output_node_names=["phase_offset"]))
self.add_network(CPPN(output_node_names=["material_present"]))
self.add_network(CPPN(output_node_names=["stiffness"]))
self.add_network(CPPN(output_node_names=["stress_adaptation_rate"]))
self.to_phenotype_mapping.add_map(name="phase_offset", tag="<PhaseOffset>")
self.to_phenotype_mapping.add_map(name="material_present", tag="<Data>", func=one_muscle, output_type=int)
self.to_phenotype_mapping.add_map(name="stiffness", tag="<Stiffness>",
func=partial(rescaled_positive_sigmoid, x_min=MIN_ELASTIC_MOD,
x_max=MAX_ELASTIC_MOD),
params=[MIN_ELASTIC_MOD, MAX_ELASTIC_MOD],
param_tags=["MinElasticMod", "MaxElasticMod"])
self.to_phenotype_mapping.add_map(name="stress_adaptation_rate", tag="<StressAdaptationRate>",
func=partial(rescaled_positive_sigmoid, x_min=-MAX_ADAPTATION_RATE,
x_max=MAX_ADAPTATION_RATE),
params=[MIN_ELASTIC_MOD, MAX_ELASTIC_MOD, MAX_STIFFNESS_CHANGE,
MAX_ADAPTATION_RATE, GROWTH_MODEL, MIN_DEVO],
param_tags=["MinElasticMod", "MaxElasticMod", "MaxStiffnessChange",
"MaxAdaptationRate", "GrowthModel", "MinDevo"])
def __init__(self):
Genotype.__init__(self, orig_size_xyz=IND_SIZE)
self.add_network(CPPN(output_node_names=["phase_offset"]))
self.add_network(CPPN(output_node_names=["material_present"]))
self.add_network(CPPN(output_node_names=["stiffness"]))
self.add_network(CPPN(output_node_names=["adaptation_rate"])
) # mapping to the phenotype is zeroed out
self.to_phenotype_mapping.add_map(name="phase_offset",
tag="<PhaseOffset>")
self.to_phenotype_mapping.add_map(name="material_present",
tag="<Data>",
func=one_muscle,
output_type=int)
self.to_phenotype_mapping.add_map(
name="stiffness",
tag="<Stiffness>",
func=partial(rescaled_positive_sigmoid,
x_min=MIN_ELASTIC_MOD,
x_max=MAX_ELASTIC_MOD),
params=[MIN_ELASTIC_MOD, MAX_ELASTIC_MOD],
param_tags=["MinElasticMod", "MaxElasticMod"])
def __init__(self):
# We instantiate a new genotype for each individual which must have the following properties
Genotype.__init__(self, orig_size_xyz=IND_SIZE)
# The genotype consists of a single Compositional Pattern Producing Network (CPPN),
# with multiple inter-dependent outputs determining the material constituting each voxel
# (e.g. two types of active voxels, actuated with a different phase, two types of passive voxels, softer and stiffer)
# The material IDs that you will see in the phenotype mapping dependencies refer to a predefined palette of materials
# currently hardcoded in tools/read_write_voxelyze.py:
# (0: empty, 1: passiveSoft, 2: passiveHard, 3: active+, 4:active-),
# but this can be changed.
self.add_network(CPPN(output_node_names=["shape", "muscleOrTissue"]))
self.to_phenotype_mapping.add_map(
name="material",
tag="<Data>",
func=make_material_tree,
dependency_order=["shape", "muscleOrTissue"],
output_type=int) # BUGFIX: "tissueType" was not listed
self.to_phenotype_mapping.add_output_dependency(name="shape",
dependency_name=None,
requirement=None,
material_if_true=None,
material_if_false="0")
self.to_phenotype_mapping.add_output_dependency(
name="muscleOrTissue",
dependency_name="shape",
requirement=True,
material_if_true=None,
material_if_false="2") # BUGFIX: was material_if_false=1
def __init__(self):
Genotype.__init__(self, orig_size_xyz=IND_SIZE)
self.add_network(CPPN(output_node_names=["initial_size"]))
self.to_phenotype_mapping.add_map(name="initial_size",
tag="<InitialVoxelSize>",
logging_stats=[
np.median, np.mean, np.std,
count_negative, count_positive
])
self.add_network(CPPN(output_node_names=["final_size"]))
self.to_phenotype_mapping.add_map(name="final_size",
tag="<FinalVoxelSize>",
logging_stats=[
np.median, np.mean, np.std,
count_negative, count_positive
])
def __init__(self):
Genotype.__init__(self, orig_size_xyz=IND_SIZE)
self.add_network(CPPN(output_node_names=["initial_size"]))
self.to_phenotype_mapping.add_map(name="initial_size", tag="<InitialVoxelSize>",
logging_stats=[np.median, np.mean, np.std, count_negative, count_positive])
self.add_network(CPPN(output_node_names=["final_size"]))
self.to_phenotype_mapping.add_map(name="final_size", tag="<FinalVoxelSize>",
logging_stats=[np.median, np.mean, np.std, count_negative, count_positive])
# self.add_network(CPPN(output_node_names=["start_growth_time"]))
# self.to_phenotype_mapping.add_map(name="start_growth_time", tag="<StartGrowthTime>", func=positive_sigmoid,
# logging_stats=[np.median, np.mean, mean_abs, np.std, std_abs])
self.add_network(CPPN(output_node_names=["growth_time"]))
self.to_phenotype_mapping.add_map(name="growth_time", tag="<GrowthTime>", func=positive_sigmoid,
logging_stats=[np.median, np.mean, mean_abs, np.std, std_abs])
def __init__(self):
Genotype.__init__(self, orig_size_xyz=IND_SIZE)
self.add_network(CPPN(output_node_names=["phase_offset"]))
self.to_phenotype_mapping.add_map(name="phase_offset", tag="<PhaseOffset>", logging_stats=None)
self.add_network(DirectEncoding(output_node_name="material_present", orig_size_xyz=IND_SIZE), freeze=True)
self.to_phenotype_mapping.add_map(name="material_present", tag="<Data>", output_type=int, logging_stats=None)
def __init__(self):
# We instantiate a new genotype for each individual which must have the following properties
Genotype.__init__(self, orig_size_xyz=IND_SIZE)
# Let's add a first CPPN to the genotype. It dictates a continuous phenotypic trait,
# the actuation phase of each voxel with respect to a global CPG-like sinusoidal signal
self.add_network(CPPN(output_node_names=["phase_offset", "frequency"]))
# Let's map this CPPN output to a VXA tag named <PhaseOffset>
self.to_phenotype_mapping.add_map(name="phase_offset", tag="<PhaseOffset>",
func=partial(rescaled_positive_sigmoid, x_min=0, x_max=2*math.pi))
self.to_phenotype_mapping.add_map(name="frequency", tag="<TempPeriod>", env_kws={"frequency": frequency_func}) # tag actually doesn't do anything here
# Now adding a second CPPN, with three outputs. "shape" the geometry of the robot
# (i.e. whether a particular voxel is empty or full),
# if full, "muscleOrTissue" dictates whether a voxel is actuated or passive. The third output, "stiffness",
# is another continuous attribute, the stiffness (elastic modulus) of each voxel
# (overrides elastic mod defined in the materials palette)
self.add_network(CPPN(output_node_names=["shape", "muscleOrTissue", "stiffness"]))
# Once remapped from [-1,1] to [MIN_ELASTIC_MOD, MAX_ELASTIC_MOD] through "func",
# the "stiffness" output goes directly to the <Stiffness> vxa tag as a continuous property.
# We also pass min and max elastic mod as sub-tags of <Stiffness> (will be used by VoxCad)
self.to_phenotype_mapping.add_map(name="stiffness", tag="<Stiffness>",
func=partial(rescaled_positive_sigmoid, x_min=MIN_ELASTIC_MOD, x_max=MAX_ELASTIC_MOD),
params=[MIN_ELASTIC_MOD, MAX_ELASTIC_MOD],
param_tags=["MinElasticMod", "MaxElasticMod"])
# The mapping for materials depends on both "shape" and "muscleOrTissue", through the following dependencies.
# Basically, if "shape" is false (cppn output < 0), the material with id "0" is assigned (-> empty voxel).
# If, instead, "shape" is true (cppn output > 0), we look at the "muscleOrTissue" output to determine
# whether the material is actuated (id = 3) or passive (id = 1). These material IDs are refer to a
# fixed palette of materials, currently hardcoded in tools/read_write_voxelyze.py
self.to_phenotype_mapping.add_map(name="material", tag="<Data>", func=make_material_tree,
dependency_order=["shape", "muscleOrTissue"], output_type=int)
self.to_phenotype_mapping.add_output_dependency(name="shape", dependency_name=None, requirement=None,
material_if_true=None, material_if_false="0")
self.to_phenotype_mapping.add_output_dependency(name="muscleOrTissue", dependency_name="shape",
requirement=True, material_if_true="3", material_if_false="1")
def __init__(self):
Genotype.__init__(self, orig_size_xyz=IND_SIZE)
# quadrupedal structure
self.add_network(DirectEncoding(output_node_name="material", orig_size_xyz=IND_SIZE), freeze=True)
self.to_phenotype_mapping.add_map(name="material", tag="<Data>", output_type=int, logging_stats=None)
# controller (phi; phase-offsets)
self.add_network(CPPN(output_node_names=["phase_offset"]))
self.to_phenotype_mapping.add_map(name="phase_offset", tag="<PhaseOffset>")
def __init__(self):
# We instantiate a new genotype for each individual which must have the following properties
Genotype.__init__(self, orig_size_xyz=IND_SIZE)
# The genotype consists of :
# - a single Compositional Pattern Producing Network (CPPN), with multiple inter-dependent outputs determining
# the material constituting each voxel (e.g. two types of active voxels, actuated with a different phase, two
# types of passive voxels, softer and stiffer). The material IDs that you will see in the phenotype mapping
# dependencies refer to a predefined palette of materials currently hardcoded in tools/read_write_voxelyze.py:
# (0: empty, 1: passiveSoft, 2: passiveHard, 3: active+, 4:active-), but this can be changed.
self.add_network(
CPPN(output_node_names=[
"shape", "newOrOld", "newMuscleOrTissue", "oldMuscleOrTissue",
"newMuscleType", "newTissueType", "muscleType", "tissueType"
]))
self.to_phenotype_mapping.add_map(name="material",
tag="<Data>",
func=make_material_tree,
dependency_order=[
"shape", "newOrOld",
"newMuscleOrTissue",
"oldMuscleOrTissue",
"newMuscleType", "newTissueType",
"muscleType", "tissueType"
],
output_type=int)
self.to_phenotype_mapping.add_output_dependency(name="shape",
dependency_name=None,
requirement=None,
material_if_true=None,
material_if_false="0")
self.to_phenotype_mapping.add_output_dependency(
name="newOrOld",
dependency_name="shape",
requirement=True,
material_if_true=None,
material_if_false=None)
self.to_phenotype_mapping.add_output_dependency(
name="newMuscleOrTissue",
dependency_name="newOrOld",
requirement=True,
material_if_true=None,
material_if_false=None)
self.to_phenotype_mapping.add_output_dependency(
name="oldMuscleOrTissue",
dependency_name="newOrOld",
requirement=False,
material_if_true=None,
material_if_false=None)
self.to_phenotype_mapping.add_output_dependency(
name="newMuscleType",
dependency_name="newMuscleOrTissue",
requirement=True,
material_if_true="8",
material_if_false="9")
self.to_phenotype_mapping.add_output_dependency(
name="newTissueType",
dependency_name="newMuscleOrTissue",
requirement=False,
material_if_true="6",
material_if_false="7")
self.to_phenotype_mapping.add_output_dependency(
name="muscleType",
dependency_name="oldMuscleOrTissue",
requirement=True,
material_if_true="3",
material_if_false="4")
self.to_phenotype_mapping.add_output_dependency(
name="tissueType",
dependency_name="oldMuscleOrTissue",
requirement=False,
material_if_true="1",
material_if_false="2")
self.materials = dict()
self.materials["6"] = NewMaterial(name="New_Passive_Soft",
rgb=(0.6, 0.3, 0),
type="tissue",
young_modulus=5000000.0,
density=1e+006,
cte=0)
self.materials["7"] = NewMaterial(name="New_Passive_Hard",
rgb=(0.3, 0, 0),
type="tissue",
young_modulus=500000000.0,
density=1e+006,
cte=0)
self.materials["8"] = NewMaterial(name="New_Active_+",
rgb=(1, 1, 0),
type="muscle",
young_modulus=5000000.0,
density=1e+006,
cte=0.05)
self.materials["9"] = NewMaterial(name="New_Active_-",
rgb=(1, 0.5, 0),
type="muscle",
young_modulus=5000000.0,
density=1e+006,
cte=-0.05)
self.materials = collections.OrderedDict(sorted(
self.materials.items()))