rofunc.learning.RofuncRL.tasks.omniisaacgymenv.shared.in_hand_manipulation#

1.  Module Contents#

1.1.  Classes#

InHandManipulationTask

1.2.  Functions#

randomize_rotation

compute_hand_reward

1.3.  API#

class rofunc.learning.RofuncRL.tasks.omniisaacgymenv.shared.in_hand_manipulation.InHandManipulationTask(name, env, offset=None)#

Bases: rofunc.learning.RofuncRL.tasks.omniisaacgym.base.rl_task.RLTask

Initialization

[summary]

set_up_scene(scene) → None#
abstract get_hand()#
abstract get_hand_view()#
abstract get_observations()#
get_object(hand_start_translation, pose_dy, pose_dz)#
get_goal()#
post_reset()#
get_object_goal_observations()#
calculate_metrics()#
pre_physics_step(actions)#
is_done()#
reset_target_pose(env_ids)#
reset_idx(env_ids)#
rofunc.learning.RofuncRL.tasks.omniisaacgymenv.shared.in_hand_manipulation.randomize_rotation(rand0, rand1, x_unit_tensor, y_unit_tensor)#
rofunc.learning.RofuncRL.tasks.omniisaacgymenv.shared.in_hand_manipulation.compute_hand_reward(rew_buf, reset_buf, reset_goal_buf, progress_buf, successes, consecutive_successes, max_episode_length: float, object_pos, object_rot, target_pos, target_rot, 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)#