curobo.opt.particle.particle_opt_utils module
- class SquashType(value)
Bases:
EnumAn enumeration.
- CLAMP = 0
- CLAMP_RESCALE = 1
- TANH = 2
- IDENTITY = 3
- scale_ctrl(
- ctrl,
- action_lows,
- action_highs,
- squash_fn: SquashType = SquashType.CLAMP,
- get_stomp_cov(
- horizon: int,
- d_action: int,
- tensor_args=TensorDeviceType(device=device(type='cuda', index=0), dtype=torch.float32, collision_geometry_dtype=torch.float32, collision_gradient_dtype=torch.float32, collision_distance_dtype=torch.float32),
- cov_mode='acc',
- RETURN_M=False,
Computes the covariance matrix following STOMP motion planner
Coefficients from here: https://en.wikipedia.org/wiki/Finite_difference_coefficient More info here: https://github.com/ros-industrial/stomp_ros/blob/7fe40fbe6ad446459d8d4889916c64e276dbf882/stomp_core/src/utils.cpp#L36
- gaussian_logprob(
- mean,
- cov,
- x,
- cov_type='full',
Calculate gaussian log prob for given input batch x :param mean (np.ndarray): :type mean (np.ndarray): [N x num_samples] batch of means :param cov (np.ndarray): :type cov (np.ndarray): [N x N] covariance matrix :param x (np.ndarray): :type x (np.ndarray): [N x num_samples] batch of sample values
- Returns:
log_prob (np.ndarray)
- Return type:
[num_sampls] log probability of each sample
- gaussian_logprobgrad(
- mean,
- cov,
- x,
- cov_type='full',
- gaussian_entropy(cov=None, L=None)
Entropy of multivariate gaussian given either covariance or cholesky decomposition of covariance
- gaussian_kl(
- mean0,
- cov0,
- mean1,
- cov1,
- cov_type='full',
KL-divergence between Gaussians given mean and covariance KL(p||q) = E_{p}[log(p) - log(q)]
- cost_to_go(
- cost_seq: Tensor,
- gamma_seq: Tensor,
- only_first: bool = False,
Calculate (discounted) cost to go for given cost sequence
- cost_to_go_np(cost_seq, gamma_seq)
Calculate (discounted) cost to go for given cost sequence
- matrix_cholesky(A)
- batch_cholesky(A)