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import torch
import torch.nn as nn
# import torch.nn.functional as F
from .gaussian_projection import GaussianProjection
[docs]class CollisionCost(nn.Module):
def __init__(self, weight=None, world_params=None, gaussian_params={}, device=torch.device('cpu'), float_dtype=torch.float32):
super(CollisionCost, self).__init__()
self.device = device
self.float_dtype = float_dtype
self.tensor_args = {'device':device, 'dtype':float_dtype}
self.weight = torch.as_tensor(weight, device=device, dtype=float_dtype)
self.proj_gaussian = GaussianProjection(gaussian_params=gaussian_params)
self.radius = []
self.position = []
for obj in world_params['world_model']['coll_objs'].keys():
self.radius.append(torch.tensor(world_params['world_model']['coll_objs'][obj]['radius'] + 0.1, **self.tensor_args))
self.position.append(torch.tensor(world_params['world_model']['coll_objs'][obj]['position'], **self.tensor_args))
[docs] def forward(self, position):
inp_device = position.device
position = position.to(self.device)
i = 0
cost = torch.norm(position - self.position[i],dim=-1) - self.radius[i]
cost[cost > 0.0] = 0.0
for i in range(1,len(self.position)):
t_cost = torch.norm(position - self.position[i],dim=-1) - self.radius[i]
t_cost[t_cost > 0.0] = 0.0
cost += t_cost
cost[cost < 0.0] = self.weight
return cost.to(inp_device)