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import torch
import torch.nn as nn
from .gaussian_projection import GaussianProjection
eps = 0.01
[docs]class ManipulabilityCost(nn.Module):
def __init__(self, ndofs, weight=None, gaussian_params={}, device=torch.device('cpu'), float_dtype=torch.float32, thresh=0.1):
super(ManipulabilityCost, self).__init__()
self.device = device
self.float_dtype = float_dtype
self.weight = torch.as_tensor(weight, device=device, dtype=float_dtype)
self.proj_gaussian = GaussianProjection(gaussian_params=gaussian_params)
self.ndofs = ndofs
self.thresh = thresh
self.i_mat = torch.ones((6,1), device=self.device, dtype=self.float_dtype)
[docs] def forward(self, jac_batch):
inp_device = jac_batch.device
with torch.cuda.amp.autocast(enabled=False):
J_J_t = torch.matmul(jac_batch, jac_batch.transpose(-2,-1))
score = torch.sqrt(torch.det(J_J_t))
score[score != score] = 0.0
score[score > self.thresh] = self.thresh #1.0
score = (self.thresh - score) / self.thresh
cost = self.weight * score
return cost.to(inp_device)