rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger

`rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger`#

1. Module Contents#

1.1. Classes#

`TrifingerDimensions`	Dimensions of the tri-finger robot.
`CuboidalObject`	Fields for a cuboidal object.
`Trifinger`

1.2. Functions#

`lgsk_kernel`	Defines logistic kernel function to bound input to [-0.25, 0)
`gen_keypoints`
`compute_trifinger_reward`
`compute_trifinger_observations_states`
`random_xy`	Returns sampled uniform positions in circle (https://stackoverflow.com/a/50746409)
`random_z`	Returns sampled height of the goal object.
`default_orientation`	Returns identity rotation transform.
`random_orientation`	Returns sampled rotation in 3D as quaternion. Ref: https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transform.Rotation.random.html
`random_orientation_within_angle`	Generates random quaternions within max_angle of base Ref: https://math.stackexchange.com/a/3448434
`random_angular_vel`	Samples a random angular velocity with standard deviation magnitude_stdev
`random_yaw_orientation`	Returns sampled rotation around z-axis.

1.3. Data#

`project_dir`
`ARENA_RADIUS`

1.4. API#

rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger.project_dir = None#

class rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger.TrifingerDimensions[source]#

Bases: enum.Enum

Dimensions of the tri-finger robot.

Note: While it may not seem necessary for tri-finger robot since it is fixed base, for floating base systems having this dimensions class is useful.

PoseDim = (7,)#

VelocityDim = 6#

StateDim = 13#

WrenchDim = 6#

NumFingers = 3#

JointPositionDim = 9#

JointVelocityDim = 9#

JointTorqueDim = 9#

GeneralizedCoordinatesDim = None#

GeneralizedVelocityDim = None#

ObjectPoseDim = 7#

ObjectVelocityDim = 6#

rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger.ARENA_RADIUS = 0.195#

class rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger.CuboidalObject(size: Union[float, Tuple[float, float, float]])[source]#

Fields for a cuboidal object.

@note Motivation for this class is that if domain randomization is performed over the: size of the cuboid, then its attributes are automatically updated as well.

Initialization

Initialize the cuboidal object.

Args:

size: The size of the object along x, y, z in meters. If a single float is provided, then it is assumed that: object is a cube.

radius_3d: float = None#

max_com_distance_to_center: float = None#

min_height: float = None#

max_height = 0.1#

NumKeypoints = 8#

ObjectPositionDim = 3#

KeypointsCoordsDim = None#

property size: Tuple[float, float, float]#: Returns the dimensions of the cuboid object (x, y, z) in meters.

class rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger.Trifinger(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render)[source]#

Bases: rofunc.learning.RofuncRL.tasks.isaacgymenv.base.vec_task.VecTask

action_dim = None#

create_sim()[source]#

compute_reward(actions)[source]#

compute_observations()[source]#

reset_idx(env_ids)[source]#

pre_physics_step(actions)[source]#

post_physics_step()[source]#

property env_steps_count: int#: Returns the total number of environment steps aggregated across parallel environments.

rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger.lgsk_kernel(x: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, scale: float = 50.0, eps: float = 2) → rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor#

Defines logistic kernel function to bound input to [-0.25, 0)

Ref: https://arxiv.org/abs/1901.08652 (page 15)

Args:: x: Input tensor. scale: Scaling of the kernel function (controls how wide the ‘bell’ shape is’) eps: Controls how ‘tall’ the ‘bell’ shape is.
Returns:: Output tensor computed using kernel.

rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger.gen_keypoints(pose: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, num_keypoints: int = 8, size: Tuple[float, float, float] = (0.065, 0.065, 0.065))#

rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger.compute_trifinger_reward(obs_buf: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, reset_buf: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, progress_buf: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, episode_length: int, dt: float, finger_move_penalty_weight: float, finger_reach_object_weight: float, object_dist_weight: float, object_rot_weight: float, env_steps_count: int, object_goal_poses_buf: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, object_state: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, last_object_state: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, fingertip_state: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, last_fingertip_state: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, use_keypoints: bool) → Tuple[rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, Dict[str, rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor]]#

rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger.compute_trifinger_observations_states(asymmetric_obs: bool, dof_position: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, dof_velocity: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, object_state: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, object_goal_poses: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, actions: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, fingertip_state: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, joint_torques: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, tip_wrenches: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor)#

rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger.random_xy(num: int, max_com_distance_to_center: float, device: str) → Tuple[rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor]#: Returns sampled uniform positions in circle (https://stackoverflow.com/a/50746409)

rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger.random_z(num: int, min_height: float, max_height: float, device: str) → rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor#: Returns sampled height of the goal object.

rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger.default_orientation(num: int, device: str) → rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor#: Returns identity rotation transform.

rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger.random_orientation(num: int, device: str) → rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor#: Returns sampled rotation in 3D as quaternion. Ref: https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transform.Rotation.random.html

rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger.random_orientation_within_angle(num: int, device: str, base: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor, max_angle: float)#: Generates random quaternions within max_angle of base Ref: https://math.stackexchange.com/a/3448434

rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger.random_angular_vel(num: int, device: str, magnitude_stdev: float) → rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor#: Samples a random angular velocity with standard deviation magnitude_stdev

rofunc.learning.RofuncRL.tasks.isaacgymenv.trifinger.random_yaw_orientation(num: int, device: str) → rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.Tensor#: Returns sampled rotation around z-axis.