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import torch
import torch.nn as nn
from .gaussian_projection import GaussianProjection
[docs]class EEVelCost(nn.Module):
def __init__(self, ndofs, device, float_dtype, weight=1.0, vec_weight=[], gaussian_params={}):
super(EEVelCost, self).__init__()
self.ndofs = ndofs
self.device = device
self.float_dtype = float_dtype
self.vel_idxs = torch.arange(self.ndofs,2*self.ndofs, dtype=torch.long, device=self.device)
# self.I = torch.eye(6, device=device)
self.I = torch.eye(ndofs, device=device, dtype=self.float_dtype)
self.vec_weight = torch.as_tensor(vec_weight, device=device, dtype=float_dtype)
self.weight = weight
self.gaussian_projection = GaussianProjection(gaussian_params=gaussian_params)
[docs] def forward(self, state_batch, jac_batch):
inp_device = state_batch.device
jac_batch = jac_batch.to(self.device)
#use jacobian to get desired delta_q
J = jac_batch
qdot = state_batch[:,:,self.ndofs:2 * self.ndofs]
xdot_current = torch.matmul(J, qdot.unsqueeze(-1)).squeeze(-1)
error = torch.sum(torch.square(self.vec_weight * xdot_current), dim=-1)
cost = self.weight * self.gaussian_projection(error)
return cost.to(inp_device)