rofunc.learning.RofuncRL.tasks.isaacgymenv.hands.qbhand_synergy_grasp

1.  Module Contents

1.1.  Classes

QbSoftHandSynergyGraspTask

This class corresponds to the GraspAndPlace task. This environment consists of dual-hands, an object and a bucket that requires us to pick up the object and put it into the bucket.

1.2.  Functions

depth_image_to_point_cloud_GPU
compute_hand_reward	Compute the reward of all environment.
randomize_rotation
randomize_rotation_pen

1.3.  API

class rofunc.learning.RofuncRL.tasks.isaacgymenv.hands.qbhand_synergy_grasp.QbSoftHandSynergyGraspTask(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render, agent_index=[[[0, 1, 2, 3, 4, 5]], [[0, 1, 2, 3, 4, 5]]], is_multi_agent=False)

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

This class corresponds to the GraspAndPlace task. This environment consists of dual-hands, an object and a bucket that requires us to pick up the object and put it into the bucket.

Initialization

Initialise the VecTask.

Args:

config: config dictionary for the environment. sim_device: the device to simulate physics on. eg. ‘cuda:0’ or ‘cpu’ graphics_device_id: the device ID to render with. headless: Set to False to disable viewer rendering. virtual_screen_capture: Set to True to allow the users get captured screen in RGB array via env.render(mode=’rgb_array’). force_render: Set to True to always force rendering in the steps (if the control_freq_inv is greater than 1 we suggest stting this arg to True)

create_sim()

Allocates which device will simulate and which device will render the scene. Defines the simulation type to be used

compute_reward(actions)

Compute the reward of all environment. The core function is compute_hand_reward(

self.rew_buf, self.reset_buf, self.reset_goal_buf, self.progress_buf, self.successes, self.consecutive_successes, self.max_episode_length, self.object_pos, self.object_rot, self.goal_pos, self.goal_rot, self.block_right_handle_pos, self.block_left_handle_pos, self.left_hand_pos, self.right_hand_pos, self.right_hand_ff_pos, self.right_hand_mf_pos, self.right_hand_rf_pos, self.right_hand_lf_pos, self.right_hand_th_pos, self.left_hand_ff_pos, self.left_hand_mf_pos, self.left_hand_rf_pos, self.left_hand_lf_pos, self.left_hand_th_pos, self.dist_reward_scale, self.rot_reward_scale, self.rot_eps, self.actions, self.action_penalty_scale, self.success_tolerance, self.reach_goal_bonus, self.fall_dist, self.fall_penalty, self.max_consecutive_successes, self.av_factor, (self.object_type == “pen”)

) , which we will introduce in detail there

Args:

actions (tensor): Actions of agents in the all environment

compute_observations()

Compute the observations of all environment. The core function is self.compute_full_state(True), which we will introduce in detail there

compute_full_state(asymm_obs=False)

Compute the observations of all environment. The observation is composed of three parts: the state values of the left and right hands, and the information of objects and target. The state values of the left and right hands were the same for each task, including hand joint and finger positions, velocity, and force information. The detail 361-dimensional observational space as shown in below:

Index Description 0 - 14 right shadow hand dof position 15 - 29 right shadow hand dof velocity 30 - 44 right shadow hand dof force 45 - 109 right shadow hand fingertip pose, linear velocity, angle velocity (5 x 13) 110 - 139 right shadow hand fingertip force, torque (5 x 6) 140 - 142 right shadow hand base position 143 - 145 right shadow hand base rotation 146 - 166 right shadow hand actions 167 - 181 left shadow hand dof position 182 - 196 left shadow hand dof velocity 197 - 211 left shadow hand dof force 212 - 276 left shadow hand fingertip pose, linear velocity, angle velocity (5 x 13) 277 - 306 left shadow hand fingertip force, torque (5 x 6) 307 - 309 left shadow hand base position 310 - 312 left shadow hand base rotation 313 - 333 left shadow hand actions 334 - 340 object pose 341 - 343 object linear velocity 344 - 346 object angle velocity 347 - 349 block right handle position 350 - 353 block right handle rotation 354 - 356 block left handle position 357 - 360 block left handle rotation

reset_target_pose(env_ids, apply_reset=False)

Reset and randomize the goal pose

Args:

env_ids (tensor): The index of the environment that needs to reset goal pose

apply_reset (bool): Whether to reset the goal directly here, usually used when the same task wants to complete multiple goals

reset_idx(env_ids, goal_env_ids)

Reset and randomize the environment

Args:

env_ids (tensor): The index of the environment that needs to reset

goal_env_ids (tensor): The index of the environment that only goals need reset

pre_physics_step(actions)

The pre-processing of the physics step. Determine whether the reset environment is needed, and calculate the next movement of Shadowhand through the given action. The 52-dimensional action space as shown in below:

Index Description 0 - 2 right shadow hand base translation 3 - 5 right shadow hand base rotation 6 - 20 right shadow hand actuated joint 21 - 23 left shadow hand base translation 24 - 26 left shadow hand base rotation 27 - 41 left shadow hand actuated joint

Args:

actions (tensor): Actions of agents in the all environment

post_physics_step()

The post-processing of the physics step. Compute the observation and reward, and visualize auxiliary lines for debug when needed

add_debug_lines(env, pos, rot)

rand_row(tensor, dim_needed)

sample_points(points, sample_num=1000, sample_mathed='furthest')

camera_visulization(is_depth_image=False)

rofunc.learning.RofuncRL.tasks.isaacgymenv.hands.qbhand_synergy_grasp.depth_image_to_point_cloud_GPU(camera_tensor, camera_view_matrix_inv, camera_proj_matrix, u, v, width: float, height: float, depth_bar: float, device: rofunc.learning.RofuncRL.tasks.utils.torch_jit_utils.torch.device)

rofunc.learning.RofuncRL.tasks.isaacgymenv.hands.qbhand_synergy_grasp.compute_hand_reward(rew_buf, reset_buf, reset_goal_buf, progress_buf, successes, consecutive_successes, max_episode_length: float, object_pos, object_rot, hand_pos, hand_ff_pos, hand_mf_pos, hand_rf_pos, hand_lf_pos, hand_th_pos, dist_reward_scale: float, rot_reward_scale: float, rot_eps: float, actions, action_penalty_scale: float, success_tolerance: float, reach_goal_bonus: float, fall_dist: float, fall_penalty: float, max_consecutive_successes: int, av_factor: float, ignore_z_rot: bool, prev_synergy_actions)

Compute the reward of all environment.

Args:

rew_buf (tensor): The reward buffer of all environments at this time

reset_buf (tensor): The reset buffer of all environments at this time

reset_goal_buf (tensor): The only-goal reset buffer of all environments at this time

progress_buf (tensor): The porgress buffer of all environments at this time

successes (tensor): The successes buffer of all environments at this time

consecutive_successes (tensor): The consecutive successes buffer of all environments at this time

max_episode_length (float): The max episode length in this environment

object_pos (tensor): The position of the object

object_rot (tensor): The rotation of the object

target_pos (tensor): The position of the target

target_rot (tensor): The rotate of the target

block_right_handle_pos (tensor): The position of the right block handle

right_hand_ff_pos, right_hand_mf_pos, right_hand_rf_pos, right_hand_lf_pos, right_hand_th_pos (tensor): The position of the five fingers

of the right hand

dist_reward_scale (float): The scale of the distance reward

rot_reward_scale (float): The scale of the rotation reward

rot_eps (float): The epsilon of the rotation calculate

actions (tensor): The action buffer of all environments at this time

action_penalty_scale (float): The scale of the action penalty reward

success_tolerance (float): The tolerance of the success determined

reach_goal_bonus (float): The reward given when the object reaches the goal

fall_dist (float): When the object is far from the Shadowhand, it is judged as falling

fall_penalty (float): The reward given when the object is fell

max_consecutive_successes (float): The maximum of the consecutive successes

av_factor (float): The average factor for calculate the consecutive successes

ignore_z_rot (bool): Is it necessary to ignore the rot of the z-axis, which is usually used

for some specific objects (e.g. pen)

rofunc.learning.RofuncRL.tasks.isaacgymenv.hands.qbhand_synergy_grasp.randomize_rotation(rand0, rand1, x_unit_tensor, y_unit_tensor)

rofunc.learning.RofuncRL.tasks.isaacgymenv.hands.qbhand_synergy_grasp.randomize_rotation_pen(rand0, rand1, max_angle, x_unit_tensor, y_unit_tensor, z_unit_tensor)