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Replay episodes from an HDF5 dataset and save videos.
Read recorded joint actions (joint_action) from record_dataset_<Task>.h5,
convert them to end-effector pose actions (EE pose actions) via forward kinematics (FK),
replay them in an environment wrapped by EE_POSE_ACTION_SPACE,
and finally save side-by-side front/wrist camera videos to disk.
"""
import os
from typing import Optional, Tuple
import cv2
import h5py
import imageio
import numpy as np
import sapien
import torch
from mani_skill.examples.motionplanning.panda.motionplanner import (
PandaArmMotionPlanningSolver,
)
from robomme.robomme_env import *
from robomme.robomme_env.utils import *
from robomme.env_record_wrapper import BenchmarkEnvBuilder
from robomme.robomme_env.utils import EE_POSE_ACTION_SPACE
from robomme.robomme_env.utils.rpy_util import build_endeffector_pose_dict
# --- Configuration ---
GUI_RENDER = True
REPLAY_VIDEO_DIR = "replay_videos"
VIDEO_FPS = 30
MAX_STEPS = 1000
def _init_fk_planner(env) -> Tuple:
"""Create PandaArmMotionPlanningSolver and return helper objects needed for FK.
Returns:
(mplib_planner, ee_link_idx, robot_base_pose)
- mplib_planner: mplib.Planner instance used for FK computation
- ee_link_idx: end-effector link index in the pinocchio model
- robot_base_pose: robot base pose in world coordinates
"""
solver = PandaArmMotionPlanningSolver(
env,
debug=False,
vis=False,
base_pose=env.unwrapped.agent.robot.pose,
visualize_target_grasp_pose=False,
print_env_info=False,
)
mplib_planner = solver.planner
ee_link_idx = mplib_planner.link_name_2_idx[mplib_planner.move_group]
robot_base_pose = env.unwrapped.agent.robot.pose
print(f"[FK] move_group: {mplib_planner.move_group}, "
f"ee_link_idx: {ee_link_idx}, "
f"link_names: {mplib_planner.user_link_names}")
return mplib_planner, ee_link_idx, robot_base_pose
def _joint_action_to_ee_pose(
mplib_planner,
joint_action: np.ndarray,
robot_base_pose: sapien.Pose,
ee_link_idx: int,
prev_ee_quat_wxyz: Optional[torch.Tensor] = None,
prev_ee_rpy_xyz: Optional[torch.Tensor] = None,
) -> Tuple[np.ndarray, torch.Tensor, torch.Tensor]:
"""Convert 8D joint action to 7D end-effector pose action via forward kinematics (FK).
Args:
mplib_planner: mplib.Planner instance (from PandaArmMotionPlanningSolver).
joint_action: 8D array [q1..q7, gripper].
robot_base_pose: robot base pose as a Sapien Pose.
ee_link_idx: end-effector link index in the pinocchio model.
prev_ee_quat_wxyz: previous-frame quaternion cache (for sign alignment).
prev_ee_rpy_xyz: previous-frame RPY cache (for continuity unwrapping).
Returns:
ee_action: 7D [x, y, z, roll, pitch, yaw, gripper].
new_prev_quat: updated quaternion cache.
new_prev_rpy: updated RPY cache.
"""
action = np.asarray(joint_action, dtype=np.float64).flatten()
arm_qpos = action[:7]
gripper = float(action[7]) if action.size > 7 else -1.0
# Build full qpos: 7 arm joints + 2 gripper finger joints
finger_pos = max(gripper, 0.0) if gripper >= 0 else 0.04
full_qpos = np.concatenate([arm_qpos, [finger_pos, finger_pos]])
# Compute forward kinematics in the robot-base coordinate frame
pmodel = mplib_planner.pinocchio_model
pmodel.compute_forward_kinematics(full_qpos)
fk_result = pmodel.get_link_pose(ee_link_idx) # 7D [x,y,z, qw,qx,qy,qz]
p_base = fk_result[:3]
q_base_wxyz = fk_result[3:] # wxyz quaternion format
# base frame -> world frame transform
pose_in_base = sapien.Pose(p_base, q_base_wxyz)
world_pose = robot_base_pose * pose_in_base
# Use shared utilities to build continuous RPY (quaternion normalization, sign alignment, RPY unwrapping)
position_t = torch.as_tensor(
np.asarray(world_pose.p, dtype=np.float64), dtype=torch.float64
)
quat_wxyz_t = torch.as_tensor(
np.asarray(world_pose.q, dtype=np.float64), dtype=torch.float64
)
pose_dict, new_prev_quat, new_prev_rpy = build_endeffector_pose_dict(
position_t, quat_wxyz_t,
prev_ee_quat_wxyz, prev_ee_rpy_xyz,
)
# Concatenate into 7D EE pose action: [position(3), RPY(3), gripper(1)]
pos_np = pose_dict["pose"].detach().cpu().numpy().flatten()[:3]
rpy_np = pose_dict["rpy"].detach().cpu().numpy().flatten()[:3]
ee_action = np.concatenate([pos_np, rpy_np, [gripper]]).astype(np.float64)
return ee_action, new_prev_quat, new_prev_rpy
def _frame_from_obs(obs: dict, is_video_frame: bool = False) -> np.ndarray:
"""Build one side-by-side frame from front and wrist camera observations."""
front = obs["front_camera"][0].cpu().numpy()
wrist = obs["wrist_camera"][0].cpu().numpy()
frame = np.concatenate([front, wrist], axis=1).astype(np.uint8)
if is_video_frame:
# Mark video-demo frames with a red border
frame = cv2.rectangle(
frame, (0, 0), (frame.shape[1], frame.shape[0]), (255, 0, 0), 10
)
return frame
def _first_execution_step(episode_data) -> int:
"""Return the first non-video-demo step index (actual execution start step)."""
step_idx = 0
while episode_data[f"timestep_{step_idx}"]["info"]["is_video_demo"][()]:
step_idx += 1
return step_idx
def process_episode(env_data: h5py.File, episode_idx: int, env_id: str) -> None:
"""Replay one episode in HDF5: read joint actions, run FK conversion, execute the environment, and save video."""
episode_data = env_data[f"episode_{episode_idx}"]
task_goal = episode_data["setup"]["task_goal"][()].decode()
total_steps = sum(1 for k in episode_data.keys() if k.startswith("timestep_"))
step_idx = _first_execution_step(episode_data)
print(f"execution start step index: {step_idx}")
# Create environment with EE_POSE_ACTION_SPACE (wrapped by EndeffectorDemonstrationWrapper)
env_builder = BenchmarkEnvBuilder(
env_id=env_id,
dataset="train",
action_space=EE_POSE_ACTION_SPACE,
gui_render=GUI_RENDER,
)
env = env_builder.make_env_for_episode(
episode_idx,
max_steps=MAX_STEPS,
include_maniskill_obs=True,
include_front_depth=True,
include_wrist_depth=True,
include_front_camera_extrinsic=True,
include_wrist_camera_extrinsic=True,
include_available_multi_choices=True,
include_front_camera_intrinsic=True,
include_wrist_camera_intrinsic=True,
)
print(f"task: {env_id}, episode: {episode_idx}, goal: {task_goal}")
obs, info = env.reset()
# Initialize FK planner (must be called after env.reset())
mplib_planner, ee_link_idx, robot_base_pose = _init_fk_planner(env)
# Observation list: length 1 means no demo video, length >1 means includes demo video; last element is current frame
frames = []
n_obs = len(obs["front_camera"])
for i in range(n_obs):
single_obs = {k: [v[i]] for k, v in obs.items()}
frames.append(_frame_from_obs(single_obs, is_video_frame=(i < n_obs - 1)))
print(f"initial frame count (demo video + current frame): {len(frames)}")
outcome = "unknown"
prev_quat: Optional[torch.Tensor] = None
prev_rpy: Optional[torch.Tensor] = None
try:
while step_idx < total_steps:
# Read joint action from HDF5
joint_action = np.asarray(
episode_data[f"timestep_{step_idx}"]["action"]["joint_action"][()],
dtype=np.float64,
)
# Forward kinematics: joint_action -> ee_pose action
ee_action, prev_quat, prev_rpy = _joint_action_to_ee_pose(
mplib_planner, joint_action, robot_base_pose, ee_link_idx,
prev_ee_quat_wxyz=prev_quat,
prev_ee_rpy_xyz=prev_rpy,
)
# Print debug info on the first step to verify FK conversion
if step_idx == _first_execution_step(episode_data):
print(f"[FK] first step joint_action: {joint_action}")
print(f"[FK] first step ee_action: {ee_action}")
# Execute EE pose action in the environment
obs, _, terminated, _, info = env.step(ee_action)
frames.append(_frame_from_obs(obs))
if GUI_RENDER:
env.render()
# TODO: hongze fix nested-list handling
if terminated:
if info.get("success", False)[-1][-1]:
outcome = "success"
if info.get("fail", False)[-1][-1]:
outcome = "fail"
break
step_idx += 1
finally:
env.close()
# Save replay video
safe_goal = task_goal.replace(" ", "_").replace("/", "_")
os.makedirs(REPLAY_VIDEO_DIR, exist_ok=True)
video_name = f"{outcome}_{env_id}_ep{episode_idx}_{safe_goal}_step-{len(frames)}.mp4"
video_path = os.path.join(REPLAY_VIDEO_DIR, video_name)
imageio.mimsave(video_path, frames, fps=VIDEO_FPS)
print(f"Video saved to {video_path}")
def replay(h5_data_dir: str = "/data/hongzefu/data_0214") -> None:
"""Iterate through all task HDF5 files in the given directory and replay episodes one by one."""
env_id_list = BenchmarkEnvBuilder.get_task_list()
for env_id in env_id_list:
file_name = f"record_dataset_{env_id}.h5"
file_path = os.path.join(h5_data_dir, file_name)
if not os.path.exists(file_path):
print(f"Skip {env_id}: file does not exist: {file_path}")
continue
with h5py.File(file_path, "r") as data:
episode_indices = sorted(
int(k.split("_")[1])
for k in data.keys()
if k.startswith("episode_")
)
print(f"task: {env_id}, total {len(episode_indices)} episodes")
for episode_idx in episode_indices:
process_episode(data, episode_idx, env_id)
if __name__ == "__main__":
import tyro
tyro.cli(replay)
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