rofunc.learning.RofuncRL.tasks.isaacgymenv.shadow_hand#

1.  Module Contents#

1.1.  Classes#

ShadowHand

1.2.  Functions#

compute_hand_reward

randomize_rotation

randomize_rotation_pen

1.3.  API#

class rofunc.learning.RofuncRL.tasks.isaacgymenv.shadow_hand.ShadowHand(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

create_sim()[source]#
compute_reward(actions)[source]#
compute_observations()[source]#
compute_fingertip_observations(no_vel=False)[source]#
compute_full_observations(no_vel=False)[source]#
compute_full_state(asymm_obs=False)[source]#
reset_target_pose(env_ids, apply_reset=False)[source]#
reset_idx(env_ids, goal_env_ids)[source]#
pre_physics_step(actions)[source]#
post_physics_step()[source]#
rofunc.learning.RofuncRL.tasks.isaacgymenv.shadow_hand.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, ignore_z_rot: bool)#
rofunc.learning.RofuncRL.tasks.isaacgymenv.shadow_hand.randomize_rotation(rand0, rand1, x_unit_tensor, y_unit_tensor)#
rofunc.learning.RofuncRL.tasks.isaacgymenv.shadow_hand.randomize_rotation_pen(rand0, rand1, max_angle, x_unit_tensor, y_unit_tensor, z_unit_tensor)#