mirage.benchmark.robosuite.evaluate_policy_demo_source_robot_server.SourceRobot#

class mirage.benchmark.robosuite.evaluate_policy_demo_source_robot_server.SourceRobot(robot_name=None, ckpt_path=None, render=False, video_path=None, rollout_horizon=None, seed=None, dataset_path=None, connection=None, port=50007, passive=True, demo_path=None, inpaint_enabled=False, forward_dynamics_model_path='', save_paired_images=False, save_paired_images_folder_path=None, device=None, save_failed_demos=False, naive=False, save_stats_path=None)#

Bases: Robot

__init__(robot_name=None, ckpt_path=None, render=False, video_path=None, rollout_horizon=None, seed=None, dataset_path=None, connection=None, port=50007, passive=True, demo_path=None, inpaint_enabled=False, forward_dynamics_model_path='', save_paired_images=False, save_paired_images_folder_path=None, device=None, save_failed_demos=False, naive=False, save_stats_path=None)#

_summary_

Args:: robot_name (string, optional): one of [Sawyer, UR5e, Panda, Kinova3, Jaco, IIWA]. If None, then the robot name is read from the checkpoint. ckpt_path (string, optional): render (bool, optional): video_path (string, optional): rollout_horizon (int, optional): seed (int, optional): connection (socket, optional):

Methods

`__init__`([robot_name, ckpt_path, render, ...])	_summary_
`compute_eef_pose`()	return a 7D or 14D pose vector
`compute_pose_error`(target_pose)
`drive_robot_to_target_pose`([target_pose, ...])
`get_object_state`()
`initialize_robot`()
`rollout_robot`([video_skip, return_obs, ...])	Helper function to carry out rollouts.
`run_experiments`(seeds[, ...])	target_robot_delta_action only affects the target robot.
`set_object_state`([set_to_target_object_state])
`set_seed`(seed)
`step`(action[, use_delta, blocking, ...])	If not blocking, action is EEF (delta) pose (6DOF) + gripper If blocking, action is EEF target state (7DOF using quarternion) + gripper

compute_eef_pose()#: return a 7D or 14D pose vector

rollout_robot(video_skip=5, return_obs=False, camera_names=None, set_object_state=False, set_robot_pose=False, tracking_error_threshold=0.003, num_iter_max=100, target_robot_delta_action=False, demo_index=0)#

Helper function to carry out rollouts. Supports on-screen rendering, off-screen rendering to a video, and returns the rollout trajectory.

The source robot will:

optionally receive the target object state and/or target robot pose from the target robot, and then set its environment to that state and/or pose.
Then, the source robot will execute the action from the policy as usual from its own environment.
Then, it will send the action command and the actual achieved pose to the target robot.

Args:

video_skip (int): how often to write video frames return_obs (bool): if True, return possibly high-dimensional observations along the trajectoryu.

They are excluded by default because the low-dimensional simulation states should be a minimal representation of the environment.

camera_names (list): determines which camera(s) are used for rendering. Pass more than: one to output a video with multiple camera views concatenated horizontally.

Returns:

stats (dict): some statistics for the rollout - such as return, horizon, and task success traj (dict): dictionary that corresponds to the rollout trajectory

run_experiments(seeds, rollout_num_episodes=1, video_skip=5, camera_names='agentview', dataset_obs=False, save_stats_path=None, tracking_error_threshold=0.003, num_iter_max=100, target_robot_delta_action=False, inpaint_online_eval=False)#: target_robot_delta_action only affects the target robot. Default is to track the absolute pose of the source robot.

step(action, use_delta=True, blocking=False, tracking_error_threshold=0.003, num_iter_max=100, goal_pose=None, name='Source Robot')#: If not blocking, action is EEF (delta) pose (6DOF) + gripper If blocking, action is EEF target state (7DOF using quarternion) + gripper