curobo.opt.particle.particle_opt_base module

class SampleMode(value)

Bases: Enum

An enumeration.

MEAN = 0
BEST = 1
SAMPLE = 2
class ParticleOptInfo(
info: Dict | None = None,
)

Bases: object

info: Dict | None = None
class ParticleOptConfig(
d_action: 'int',
action_lows: 'List[float]',
action_highs: 'List[float]',
action_horizon: 'int',
horizon: 'int',
n_iters: 'int',
cold_start_n_iters: 'Union[int, None]',
rollout_fn: 'RolloutBase',
tensor_args: 'TensorDeviceType',
use_cuda_graph: 'bool',
store_debug: 'bool',
debug_info: 'Any',
n_problems: 'int',
num_particles: 'Union[int, None]',
sync_cuda_time: 'bool',
use_coo_sparse: 'bool',
gamma: float,
sample_mode: curobo.opt.particle.particle_opt_base.SampleMode,
seed: int,
calculate_value: bool,
store_rollouts: bool,
)

Bases: OptimizerConfig

gamma: float
sample_mode: SampleMode
seed: int
calculate_value: bool
store_rollouts: bool
static create_data_dict(
data_dict: Dict,
rollout_fn: RolloutBase,
tensor_args: TensorDeviceType = 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),
child_dict: Dict | None = None,
)

Helper function to create dictionary from optimizer parameters and rollout class.

Parameters:

  • data_dict – optimizer parameters dictionary.

  • rollout_fn – rollout function.

  • tensor_args – tensor cuda device.

  • child_dict – new dictionary where parameters will be stored.

Returns:

Dictionary with parameters to create a OptimizerConfig

d_action: int

Number of optimization variables per timestep.

action_lows: List[float]

Lower bound for optimization variables.

action_highs: List[float]

Higher bound for optimization variables

action_horizon: int
horizon: int

Number of timesteps in optimization state, total variables = d_action * horizon

n_iters: int

Number of iterations to run optimization

cold_start_n_iters: int | None

Number of iterations to run optimization during the first instance. Setting to None will use n_iters. This parameter is useful in MPC like settings where we need to run many iterations during initialization (cold start) and then run only few iterations (warm start).

rollout_fn: RolloutBase

Rollout function to use for computing cost, given optimization variables.

tensor_args: TensorDeviceType

Tensor device to use for optimization.

use_cuda_graph: bool

Capture optimization iteration in a cuda graph and replay graph instead of eager execution. Enabling this can make optimization 10x faster. But changing control flow, tensor shapes, or problem type is not allowed.

store_debug: bool

Record debugging data such as optimization variables, and cost at every iteration. Enabling this will disable cuda graph.

debug_info: Any

Use this to record additional attributes from rollouts.

n_problems: int

Number of parallel problems to optimize.

num_particles: int | None

Number of particles to use per problem. Common optimization solvers use many particles to optimize a single problem. E.g., MPPI rolls out many parallel samples and computes a weighted mean. In cuRobo, Quasi-Newton solvers use particles to run many line search magnitudes. Total optimization batch size = n_problems * num_particles.

sync_cuda_time: bool

Synchronize device before computing solver time.

use_coo_sparse: bool

Matmul with a Sparse tensor is used to create particles for each problem index to save memory and compute. Some versions of pytorch do not support coo sparse, specifically during torch profile runs. Set this to False to use a standard tensor.

class ParticleOptBase(
config: ParticleOptConfig | None = None,
)

Bases: Optimizer, ParticleOptConfig

Base class for sampling based controllers.

Base optimization solver class

Parameters:

config – Initialized with parameters from a dataclass.

abstract _get_action_seq(
mode=<enum 'SampleMode'>,
)

Get action sequence to execute on the system based on current control distribution

Parameters:

mode – {‘mean’, ‘sample’} how to choose action to be executed ‘mean’ plays mean action and ‘sample’ samples from the distribution

abstract sample_actions(
init_act: Tensor,
)

Sample actions from current control distribution

update_seed(init_act)
abstract _update_distribution(
trajectories: Trajectory,
)

Update current control distribution using rollout trajectories

Parameters:

trajectories –

dict Rollout trajectories. Contains the following fields observations : torch.tensor

observations along rollouts

actionstorch.tensor

actions sampled from control distribution along rollouts

coststorch.tensor

step costs along rollouts

reset()

Reset the optimizer

abstract _calc_val(
trajectories: Trajectory,
)

Calculate value of state given rollouts from a policy

check_convergence()

Checks if controller has converged Returns False by default

generate_rollouts(
init_act=None,
)

Samples a batch of actions, rolls out trajectories for each particle and returns the resulting observations, costs, actions

Parameters:

state (dict or np.ndarray) – Initial state to set the simulation problem to

_optimize(
init_act: Tensor,
shift_steps=0,
n_iters=None,
)

Optimize for best action at current state

Parameters:

  • state (torch.Tensor) – state to calculate optimal action from

  • calc_val (bool) – If true, calculate the optimal value estimate of the state along with action

Returns:

  • action (torch.Tensor) – next action to execute

  • value (float) – optimal value estimate (default: 0.)

  • info (dict) – dictionary with side-information

_initialize_cuda_graph(
init_act: Tensor,
shift_steps=0,
)
_call_cuda_opt_iters(
init_act: Tensor,
)
_run_opt_iters(
init_act: Tensor,
shift_steps=0,
n_iters=None,
)
update_nproblems(
n_problems,
)

Update the number of problems that need to be optimized.

Parameters:

n_problems – number of problems.

abstract _shift(shift_steps=0)

Shift the variables in the solver to hotstart the next timestep.

Parameters:

shift_steps – Number of timesteps to shift.

_update_problem_kernel(
n_problems: int,
num_particles: int,
)

Update matrix used to map problem index to number of particles.

Parameters:

  • n_problems – Number of optimization problems.

  • num_particles – Number of particles per problem.

static create_data_dict(
data_dict: Dict,
rollout_fn: RolloutBase,
tensor_args: TensorDeviceType = 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),
child_dict: Dict | None = None,
)

Helper function to create dictionary from optimizer parameters and rollout class.

Parameters:

  • data_dict – optimizer parameters dictionary.

  • rollout_fn – rollout function.

  • tensor_args – tensor cuda device.

  • child_dict – new dictionary where parameters will be stored.

Returns:

Dictionary with parameters to create a OptimizerConfig

get_all_rollout_instances() → List[RolloutBase]

Get all instances of Rollout class in the optimizer.

get_nproblem_tensor(x)

This function takes an input tensor of shape (n_problem,….) and converts it into (n_particles,…).

optimize(
opt_tensor: Tensor,
shift_steps=0,
n_iters=None,
) → Tensor

Find a solution through optimization given the initial values for variables.

Parameters:

  • opt_tensor – Initial value of optimization variables. Shape: [n_problems, action_horizon, d_action]

  • shift_steps – Shift variables along action_horizon. Useful in mpc warm-start setting.

  • n_iters – Override number of iterations to run optimization.

Returns:

Optimized values returned as a tensor of shape [n_problems, action_horizon, d_action].

reset_cuda_graph()

Reset CUDA Graphs. This does not work, workaround is to create a new instance.

reset_seed()

Reset seeds.

reset_shape()

Reset any flags in rollout class. Useful to reinitialize tensors for a new shape.

update_num_particles_per_problem(
num_particles_per_problem,
)
update_params(
goal: Goal,
)

Update parameters in the curobo.rollout.rollout_base.RolloutBase instance.

Parameters:

goal – parameters to update rollout instance.

d_action: int

Number of optimization variables per timestep.

action_lows: List[float]

Lower bound for optimization variables.

action_highs: List[float]

Higher bound for optimization variables

action_horizon: int
horizon: int

Number of timesteps in optimization state, total variables = d_action * horizon

n_iters: int

Number of iterations to run optimization

cold_start_n_iters: int | None

Number of iterations to run optimization during the first instance. Setting to None will use n_iters. This parameter is useful in MPC like settings where we need to run many iterations during initialization (cold start) and then run only few iterations (warm start).

rollout_fn: RolloutBase

Rollout function to use for computing cost, given optimization variables.

tensor_args: TensorDeviceType

Tensor device to use for optimization.

use_cuda_graph: bool

Capture optimization iteration in a cuda graph and replay graph instead of eager execution. Enabling this can make optimization 10x faster. But changing control flow, tensor shapes, or problem type is not allowed.

store_debug: bool

Record debugging data such as optimization variables, and cost at every iteration. Enabling this will disable cuda graph.

debug_info: Any

Use this to record additional attributes from rollouts.

n_problems: int

Number of parallel problems to optimize.

num_particles: int | None

Number of particles to use per problem. Common optimization solvers use many particles to optimize a single problem. E.g., MPPI rolls out many parallel samples and computes a weighted mean. In cuRobo, Quasi-Newton solvers use particles to run many line search magnitudes. Total optimization batch size = n_problems * num_particles.

sync_cuda_time: bool

Synchronize device before computing solver time.

use_coo_sparse: bool

Matmul with a Sparse tensor is used to create particles for each problem index to save memory and compute. Some versions of pytorch do not support coo sparse, specifically during torch profile runs. Set this to False to use a standard tensor.

gamma: float
sample_mode: SampleMode
seed: int
calculate_value: bool
store_rollouts: bool