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add replay script

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Zhu Juan
2026-06-25 17:48:29 +08:00
parent e99f6a0589
commit 7017370142
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benchmark_replay.yaml Normal file
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general:
root_paths:
asset: /home/zhiyuan/zhujuan/joysim_exp/gen_data/data # Root directory for assets (robots, objects, scene USDs, etc.)
checkpoints: /home/zhiyuan/zhujuan/checkpoints
output: /home/zhiyuan/zhujuan/joysim_exp/output # Root directory for outputs (recorded data, logs, etc.)
simulation:
stereotype: isaaclab
launch_config:
device: cuda
enable_cameras: true
headless: false
livestream: 0
scene:
name: kujiale_multispace
base_config:
stereotype: usd
name: _827313_home_workspace_00
source: platform
asset_path: platform://scenes/kujiale_multispace/827313_home/workspace_00.usd
object_cfg_dict:
omni6DPose_can_016:
name: omni6DPose_can_016
stereotype: rigid
source: local
asset_path: asset://objects/omni6DPose/can/omni6DPose_can_016/Aligned.usd
scale:
- 0.001
- 0.001
- 0.001
position:
- 0.15
- -4.02430000000001
- 0.510259093
rotation:
- -0.304408012043137
- -0.304408012043137
- 0.638228612805745
- 0.6382286128057448
omni6DPose_book_031:
name: omni6DPose_book_031
stereotype: rigid
source: local
asset_path: asset://objects/omni6DPose/book/omni6DPose_book_031/Aligned.usd
scale:
- 0.001
- 0.001
- 0.001
position:
- 0.15
- -4.152430000000001
- 0.510259093
quaternion: [1, 0, 0, 0]
axis_y_up: true
robot_cfg_dict:
r1pro_dex:
name: r1pro_dex
asset_path: asset://robots/r1pro/r1pro_dex.usd
position: [-0.2, -4.1, 0.0]
rotation: [1, 0, 0, 0]
stereotype: modular_robot
source: local
init_joint_position:
torso_joint1: 0.0
torso_joint2: 0.0
torso_joint3: 0.0
torso_joint4: 0.0
left_arm_joint1: -0.2
left_arm_joint2: 0.05
left_arm_joint3: 0.0
left_arm_joint4: -1.0
left_arm_joint5: 0.0
left_arm_joint6: 0.0
left_arm_joint7: 0.0
right_arm_joint1: -0.2
right_arm_joint2: -0.05
right_arm_joint3: 0.0
right_arm_joint4: -1.0
right_arm_joint5: 0.1
right_arm_joint6: 0.0
right_arm_joint7: 0.0
actuator_cfg_dict:
left_arm:
stereotype: arm
joint_names_expr: [left_arm_joint1, left_arm_joint2, left_arm_joint3, left_arm_joint4, left_arm_joint5, left_arm_joint6, left_arm_joint7]
stiffness: 60000.0
damping: 4000.0
right_arm:
stereotype: arm
joint_names_expr: [right_arm_joint1, right_arm_joint2, right_arm_joint3, right_arm_joint4, right_arm_joint5, right_arm_joint6, right_arm_joint7]
stiffness: 60000.0
damping: 4000.0
left_hand:
stereotype: arm
joint_names_expr: [left_thumb_CMC_FE, left_thumb_CMC_AA, left_thumb_MCP_FE, left_thumb_MCP_AA, left_thumb_IP, left_index_MCP_FE, left_index_MCP_AA, left_index_PIP, left_index_DIP, left_middle_MCP_FE, left_middle_MCP_AA, left_middle_PIP, left_middle_DIP, left_ring_MCP_FE, left_ring_MCP_AA, left_ring_PIP, left_ring_DIP, left_pinky_CMC, left_pinky_MCP_FE, left_pinky_MCP_AA, left_pinky_PIP, left_pinky_DIP]
stiffness: 50.0
damping: 5.0
right_hand:
stereotype: arm
joint_names_expr: [right_thumb_CMC_FE, right_thumb_CMC_AA, right_thumb_MCP_FE, right_thumb_MCP_AA, right_thumb_IP, right_index_MCP_FE, right_index_MCP_AA, right_index_PIP, right_index_DIP, right_middle_MCP_FE, right_middle_MCP_AA, right_middle_PIP, right_middle_DIP, right_ring_MCP_FE, right_ring_MCP_AA, right_ring_PIP, right_ring_DIP, right_pinky_CMC, right_pinky_MCP_FE, right_pinky_MCP_AA, right_pinky_PIP, right_pinky_DIP]
stiffness: 50.0
damping: 5.0
torso:
stereotype: arm
joint_names_expr: [torso_joint1, torso_joint2, torso_joint3, torso_joint4]
stiffness: 100000.0
damping: 8000.0
base_lock:
stereotype: arm
joint_names_expr: [steer_motor_joint1, steer_motor_joint2, steer_motor_joint3, wheel_motor_joint1, wheel_motor_joint2, wheel_motor_joint3]
stiffness: 100000.0
damping: 5000.0
arm_modules:
left_arm:
arm_actuator_name: left_arm
ee_link_name: left_hand_C_MC
ee_type: dexterous_hand
ee_actuator_name: left_hand
right_arm:
arm_actuator_name: right_arm
ee_link_name: right_hand_C_MC
ee_type: dexterous_hand
ee_actuator_name: right_hand
extra_modules:
torso:
actuator_name: torso
use_planner: false
sensor_cfg_dict:
head_camera:
name: head_camera
stereotype: camera
data_types: [rgb]
width: 1280
height: 720
camera_model: pinhole
fix_camera: true
focal_length: 2.8
horizontal_aperture: 4.890881131191918
vertical_aperture: 2.7608816125932627
convention: opengl
attach_to:
target_name: r1pro_dex
is_articulation_part: true
articulation_part_name: zed_link
create_fixed_joint: true
local_position: [0.0, 0.0, 0.0]
local_rotation:
- 0.33
- 1.0
- -0.0
- 0.0
front_camera:
name: front_camera
stereotype: camera
position: [2, -4.1, 1.8]
look_at:
is_point: true
look_at_point: [0.0, -4.1, 1.2]
data_types: [rgb]
width: 1280
height: 720
camera_model: pinhole
fix_camera: true
left_camera:
name: left_camera
stereotype: camera
position: [-0.58554, -2.0, 1.8]
look_at:
is_point: true
look_at_point: [0.0, -4.1, 1.2]
data_types: [rgb]
width: 1280
height: 720
camera_model: pinhole
fix_camera: true
right_camera:
name: right_camera
stereotype: camera
position: [0.36816, -5.36, 1.8]
look_at:
is_point: true
look_at_point: [0.0, -4.1, 1.2]
data_types: [rgb]
width: 1280
height: 720
camera_model: pinhole
fix_camera: true
light_cfg_dict:
sun:
name: sun
stereotype: general_light
light_type: distant
position: [0, 0, 5]
rotation: [1, 0, 0, 0]
intensity: 1000
angle: 0.53
color: [1.0, 1.0, 1.0]
sky:
name: sky
stereotype: general_light
light_type: dome
intensity: 10.0
color: [1.0, 1.0, 1.0]
extension:
extension_cfg_dict:
replay_data_collect:
enable: true
stereotype: data_collect
observer_cfgs:
- stereotype: robot_observer
name: r1pro_dex
target_joint_names:
- left_thumb_CMC_FE
- left_thumb_CMC_AA
- left_thumb_MCP_FE
- left_thumb_MCP_AA
- left_thumb_IP
- left_index_MCP_FE
- left_index_MCP_AA
- left_index_PIP
- left_index_DIP
- left_middle_MCP_FE
- left_middle_MCP_AA
- left_middle_PIP
- left_middle_DIP
- left_ring_MCP_FE
- left_ring_MCP_AA
- left_ring_PIP
- left_ring_DIP
- left_pinky_CMC
- left_pinky_MCP_FE
- left_pinky_MCP_AA
- left_pinky_PIP
- left_pinky_DIP
- right_thumb_CMC_FE
- right_thumb_CMC_AA
- right_thumb_MCP_FE
- right_thumb_MCP_AA
- right_thumb_IP
- right_index_MCP_FE
- right_index_MCP_AA
- right_index_PIP
- right_index_DIP
- right_middle_MCP_FE
- right_middle_MCP_AA
- right_middle_PIP
- right_middle_DIP
- right_ring_MCP_FE
- right_ring_MCP_AA
- right_ring_PIP
- right_ring_DIP
- right_pinky_CMC
- right_pinky_MCP_FE
- right_pinky_MCP_AA
- right_pinky_PIP
- right_pinky_DIP
observe_ee_pose: true
observe_ee_state: true
observe_joint_position: true
observe_joint_velocity: true
observe_joint_positions: true
observe_joint_velocities: true
observe_joint_accelerations: true
observe_joint_position_targets: true
observe_joint_velocity_targets: true
- stereotype: sensor_observer
name: head_camera
observe_rgb: true
- stereotype: sensor_observer
name: front_camera
observe_rgb: true
- stereotype: sensor_observer
name: left_camera
observe_rgb: true
- stereotype: sensor_observer
name: right_camera
observe_rgb: true
record:
enable: true # set to true to record the data
stereotype: record
data_collector_name: replay_data_collect
record_backend: lfs
record_fps: 30
use_recorder_step: true
backend_root_path: output://replay_egodex
postprocess_list: ["hdf5", "video", "preview_video"]

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#!/usr/bin/env python
"""Replay a LeRobot (egodex-style) bimanual trajectory on r1pro_dex in fastsim,
and record the result via the `record` extension.
The source dataset stores, per frame, a 56-D ``observation.state``:
[ 0: 3] left wrist position (xyz) |
[ 3: 6] left wrist orientation (euler xyz)| left arm -> solved with IK
[ 6:28] left hand 22 joint angles -> set directly
[28:31] right wrist position (xyz) |
[31:34] right wrist orientation (euler xyz)| right arm -> solved with IK
[34:56] right hand 22 joint angles -> set directly
Because the wrist poses are expressed in the capture/camera frame (unknown
transform to the robot base), the arms are driven by *delta poses*: each frame's
target EE pose in the robot base frame is
target_base[t] = base_ee_init * ( cam_ee[0]^-1 * cam_ee[t] )
i.e. the trajectory's motion relative to its own first frame (expressed in the
EE-local frame, which is camera-frame-independent) applied on top of the robot's
actual initial EE pose. The 7-DoF arm joints are recovered with fastsim's
per-arm differential IK; the simulation runs physics-disabled (pure kinematic
playback) at dt=1/60 so each 15-fps data frame gets ~4 IK convergence steps.
Objects are NOT replayed (per requirement).
Usage (must use the fastsim conda env's python):
python replay_lerobot.py --episode 0
python replay_lerobot.py --all # one subprocess per episode
"""
import argparse
import os
import subprocess
import sys
import tempfile
import numpy as np
# --------------------------------------------------------------------------- #
# Constants
# --------------------------------------------------------------------------- #
HERE = os.path.dirname(os.path.abspath(__file__))
DEFAULT_DATASET = os.path.join(HERE, "extracted", "add_remove_lid_15fps_10epi")
DEFAULT_CONFIG = os.path.join(HERE, "replay_config.yaml")
DATA_FPS = 15
ROBOT_NAME = "r1pro_dex"
# 22 hand joints per side, in the exact order the dataset packs them
# (matches r1pro_dex.usd / benchmark.yaml actuator lists: thumb5, index4,
# middle4, ring4, pinky5).
LEFT_HAND_JOINTS = [
"left_thumb_CMC_FE", "left_thumb_CMC_AA", "left_thumb_MCP_FE", "left_thumb_MCP_AA", "left_thumb_IP",
"left_index_MCP_FE", "left_index_MCP_AA", "left_index_PIP", "left_index_DIP",
"left_middle_MCP_FE", "left_middle_MCP_AA", "left_middle_PIP", "left_middle_DIP",
"left_ring_MCP_FE", "left_ring_MCP_AA", "left_ring_PIP", "left_ring_DIP",
"left_pinky_CMC", "left_pinky_MCP_FE", "left_pinky_MCP_AA", "left_pinky_PIP", "left_pinky_DIP",
]
RIGHT_HAND_JOINTS = [n.replace("left_", "right_", 1) for n in LEFT_HAND_JOINTS]
# 56-D observation.state slices
SLICES = {
"left_arm": {"pos": slice(0, 3), "euler": slice(3, 6), "hand": slice(6, 28)},
"right_arm": {"pos": slice(28, 31), "euler": slice(31, 34), "hand": slice(34, 56)},
}
HAND_JOINTS = {"left_arm": LEFT_HAND_JOINTS, "right_arm": RIGHT_HAND_JOINTS}
ARMS = ["left_arm", "right_arm"]
# --- Arm targeting -------------------------------------------------------- #
# Delta-pose replay (camera pose is IGNORED): for each frame compute the relative
# transform from frame 0 in the data's own frame, then apply it on top of the
# robot's actual initial EE pose:
# delta_t = cam_ee[0]^-1 * cam_ee[t] # relative motion, in EE-local frame
# target_t = ee_init * delta_t # applied to the robot's init EE pose
# Frame 0 -> delta = identity -> robot starts exactly at its init EE pose.
# Optional fixed correction between the dataset wrist frame and r1pro's ee_link.
EE_OFFSET = {
"left_arm": np.eye(4),
"right_arm": np.eye(4),
}
# --------------------------------------------------------------------------- #
# Data loading
# --------------------------------------------------------------------------- #
def load_episode(dataset_dir: str, episode: int) -> np.ndarray:
"""Return (N, 56) float32 states for one episode, ordered by frame_index."""
import pyarrow.parquet as pq
data_file = os.path.join(dataset_dir, "data", "chunk-000", "file-000.parquet")
df = pq.read_table(data_file).to_pandas()
ep = df[df["episode_index"] == episode].sort_values("frame_index")
if len(ep) == 0:
raise ValueError(f"episode {episode} not found in {data_file}")
states = np.stack(ep["observation.state"].to_numpy()).astype(np.float64)
assert states.shape[1] == 56, f"expected 56-D state, got {states.shape}"
return states
def build_temp_config(base_config: str, out_subdir: str) -> str:
"""Clone the YAML config, redirecting the recorder output to a per-episode dir."""
import yaml
with open(base_config) as f:
cfg = yaml.safe_load(f)
rec = cfg["extension"]["extension_cfg_dict"]["record"]
rec["backend_root_path"] = f"output://{out_subdir}"
fd, path = tempfile.mkstemp(suffix=".yaml", prefix="replay_cfg_")
with os.fdopen(fd, "w") as f:
yaml.safe_dump(cfg, f, sort_keys=False)
return path
# --------------------------------------------------------------------------- #
# Single-episode replay (runs inside fastsim)
# --------------------------------------------------------------------------- #
def run_episode(dataset_dir: str, episode: int, base_config: str):
states = load_episode(dataset_dir, episode)
n_frames = len(states)
dataset_name = os.path.basename(dataset_dir.rstrip("/"))
out_subdir = f"benchmark_replay_record/{dataset_name}/episode_{episode:02d}"
temp_config = build_temp_config(base_config, out_subdir)
# Heavy sim imports happen only now (after the lightweight data load).
from fastsim.app import FastSim
from fastsim.unisim.scene_manager import SceneManager
from fastsim.utils.pose import Pose
from fastsim.utils.log import Log
# Pre-build camera-frame EE poses + hand targets per arm.
cam_ee = {arm: [] for arm in ARMS}
hand_targets = {arm: [] for arm in ARMS}
for s in states:
for arm in ARMS:
sl = SLICES[arm]
cam_ee[arm].append(
Pose.from_euler_xyz(position=s[sl["pos"]].tolist(),
euler_xyz=s[sl["euler"]].tolist())
)
hand_targets[arm].append(s[sl["hand"]].tolist())
sim = FastSim(temp_config)
sim.set_physics_disabled(True) # pure kinematic playback
ee_off = {arm: Pose.from_homogeneous_matrix(EE_OFFSET[arm]) for arm in ARMS}
state = {"base_ee_init": {}, "cam0_inv": {}, "ready": False}
def on_post_reset():
"""Anchor the delta-pose replay to the robot's actual init EE pose."""
robot = SceneManager.get_robot(ROBOT_NAME)
for arm in ARMS:
state["base_ee_init"][arm] = robot.get_ee_pose(arm_name=arm)
state["cam0_inv"][arm] = cam_ee[arm][0].inverse()
state["ready"] = True
Log.info(f"[replay] episode {episode}: {n_frames} frames @ {DATA_FPS}fps; "
f"delta-pose replay (camera pose ignored)", title="Replay")
def apply_frame(robot, idx):
for arm in ARMS:
# delta = cam_ee[0]^-1 * cam_ee[idx]; target = ee_init * delta * ee_off
delta = state["cam0_inv"][arm] * cam_ee[arm][idx]
target = state["base_ee_init"][arm] * (delta * ee_off[arm])
ik = robot.solve_ik(target, arm_name=arm)
jpos = np.asarray(ik["joint_position"]).reshape(-1).tolist()
robot.set_joint_position(jpos, ik["joint_names"])
robot.set_joint_position_target(jpos, ik["joint_names"])
# hand joints: exact angles, set directly
robot.set_joint_position(hand_targets[arm][idx], HAND_JOINTS[arm])
robot.set_joint_position_target(hand_targets[arm][idx], HAND_JOINTS[arm])
def on_step(ctx):
if not state["ready"]:
return
interval = max(1, round(1.0 / (ctx.dt * DATA_FPS)))
# Allow one extra interval so the recorder (which captures every
# `interval` steps) flushes the final converged frame.
if ctx.step > (n_frames + 1) * interval:
sim.request_terminate()
return
idx = min(ctx.step // interval, n_frames - 1)
robot = SceneManager.get_robot(ROBOT_NAME)
apply_frame(robot, idx)
sim.add_post_reset_callback(on_post_reset)
sim.add_step_callback(on_step)
Log.info(f"[replay] output -> output://{out_subdir}", title="Replay")
sim.start() # setup + loop; calls os._exit(0) on finish
# --------------------------------------------------------------------------- #
# Multi-episode driver (subprocess per episode, since FastSim os._exit()s)
# --------------------------------------------------------------------------- #
def run_all(dataset_dir: str, base_config: str, episodes):
for ep in episodes:
print(f"\n========== EPISODE {ep} ==========", flush=True)
rc = subprocess.run(
[sys.executable, os.path.abspath(__file__),
"--dataset", dataset_dir, "--config", base_config, "--episode", str(ep)]
).returncode
# FastSim exits via os._exit(0); a non-zero code means a real failure.
if rc not in (0,):
print(f"[replay] episode {ep} exited with code {rc}", flush=True)
def list_episodes(dataset_dir: str):
import json
info = json.load(open(os.path.join(dataset_dir, "meta", "info.json")))
return list(range(int(info["total_episodes"])))
def main():
ap = argparse.ArgumentParser(description=__doc__)
ap.add_argument("--dataset", default=DEFAULT_DATASET, help="LeRobot dataset dir")
ap.add_argument("--config", default=DEFAULT_CONFIG, help="fastsim YAML config")
ap.add_argument("--episode", type=int, default=None, help="single episode index")
ap.add_argument("--all", action="store_true", help="replay every episode")
args = ap.parse_args()
if args.all:
run_all(args.dataset, args.config, list_episodes(args.dataset))
else:
ep = 0 if args.episode is None else args.episode
run_episode(args.dataset, ep, args.config)
if __name__ == "__main__":
main()

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# ============================================================
# R1Pro-Dex LeRobot replay config (fastsim)
# Derived from benchmark.yaml, adapted for offline trajectory
# replay driven by replay_lerobot.py.
#
# Differences vs benchmark.yaml (intentional):
# - root_paths fixed for THIS machine.
# - kujiale scene + can/book objects dropped (objects are NOT
# replayed; the lid-on-cup trajectory is unrelated to that room),
# replaced by a ground plane for fast load & clear viewing.
# - NO replay / benchmark / policy_server extensions: the motion is
# driven from Python (hands set directly, arms solved via IK).
# - only data_collect + record extensions remain, to record the
# replayed motion back out (hdf5 + video).
# ============================================================
general:
root_paths:
asset: /home/zhiyuan/zhujuan/joysim_exp/gen_data/data
output: /home/zhiyuan/zhujuan/joysim_exp/output
simulation:
stereotype: isaaclab
dt: 0.008333333333333333 # 1/120 -> 8 sim steps per 15fps data frame (more IK convergence)
launch_config:
device: cuda
enable_cameras: true
headless: true # set false to watch live; true records faster
livestream: 0
scene:
name: r1pro_dex_lerobot_replay
position: [0, 0, 0]
rotation: [1, 0, 0, 0]
base_config:
name: default_base
stereotype: ground_plane
ground_plane_size: [100, 100]
robot_cfg_dict:
r1pro_dex:
name: r1pro_dex
asset_path: asset://robots/r1pro/r1pro_dex.usd
position: [0, 0, 0]
rotation: [1, 0, 0, 0]
stereotype: modular_robot
source: local
# init pose copied from benchmark.yaml (defines the starting EE pose
# that the delta-pose arm replay is anchored to).
init_joint_position:
torso_joint1: 0.0
torso_joint2: 0.0
torso_joint3: 0.0
torso_joint4: 0.0
# palms-down init (back of hand up): verified ee_link +X palm-normal -> ~[0,0,-1]
# (down-score 0.97 on both hands), within joint limits.
left_arm_joint1: -0.2
left_arm_joint2: 0.6
left_arm_joint3: 0.0
left_arm_joint4: -1.6
left_arm_joint5: -2.3
left_arm_joint6: -0.33
left_arm_joint7: 0.0
right_arm_joint1: -0.2
right_arm_joint2: -0.6
right_arm_joint3: 0.0
right_arm_joint4: -1.6
right_arm_joint5: 2.3
right_arm_joint6: -0.33
right_arm_joint7: 0.0
actuator_cfg_dict:
left_arm:
stereotype: arm
joint_names_expr: [left_arm_joint1, left_arm_joint2, left_arm_joint3, left_arm_joint4, left_arm_joint5, left_arm_joint6, left_arm_joint7]
stiffness: 60000.0
damping: 4000.0
right_arm:
stereotype: arm
joint_names_expr: [right_arm_joint1, right_arm_joint2, right_arm_joint3, right_arm_joint4, right_arm_joint5, right_arm_joint6, right_arm_joint7]
stiffness: 60000.0
damping: 4000.0
left_hand:
stereotype: arm
joint_names_expr: [left_thumb_CMC_FE, left_thumb_CMC_AA, left_thumb_MCP_FE, left_thumb_MCP_AA, left_thumb_IP, left_index_MCP_FE, left_index_MCP_AA, left_index_PIP, left_index_DIP, left_middle_MCP_FE, left_middle_MCP_AA, left_middle_PIP, left_middle_DIP, left_ring_MCP_FE, left_ring_MCP_AA, left_ring_PIP, left_ring_DIP, left_pinky_CMC, left_pinky_MCP_FE, left_pinky_MCP_AA, left_pinky_PIP, left_pinky_DIP]
stiffness: 50.0
damping: 5.0
right_hand:
stereotype: arm
joint_names_expr: [right_thumb_CMC_FE, right_thumb_CMC_AA, right_thumb_MCP_FE, right_thumb_MCP_AA, right_thumb_IP, right_index_MCP_FE, right_index_MCP_AA, right_index_PIP, right_index_DIP, right_middle_MCP_FE, right_middle_MCP_AA, right_middle_PIP, right_middle_DIP, right_ring_MCP_FE, right_ring_MCP_AA, right_ring_PIP, right_ring_DIP, right_pinky_CMC, right_pinky_MCP_FE, right_pinky_MCP_AA, right_pinky_PIP, right_pinky_DIP]
stiffness: 50.0
damping: 5.0
torso:
stereotype: arm
joint_names_expr: [torso_joint1, torso_joint2, torso_joint3, torso_joint4]
stiffness: 100000.0
damping: 8000.0
base_lock:
stereotype: arm
joint_names_expr: [steer_motor_joint1, steer_motor_joint2, steer_motor_joint3, wheel_motor_joint1, wheel_motor_joint2, wheel_motor_joint3]
stiffness: 100000.0
damping: 5000.0
arm_modules:
left_arm:
arm_actuator_name: left_arm
ee_link_name: left_hand_C_MC
ee_type: dexterous_hand
ee_actuator_name: left_hand
right_arm:
arm_actuator_name: right_arm
ee_link_name: right_hand_C_MC
ee_type: dexterous_hand
ee_actuator_name: right_hand
extra_modules:
torso:
actuator_name: torso
use_planner: false
sensor_cfg_dict:
# Ego/head view, attached to the robot head — comparable to the source
# dataset's observation.images.ego_view.
head_camera:
name: head_camera
stereotype: camera
data_types: [rgb]
width: 1280
height: 720
camera_model: pinhole
fix_camera: true
focal_length: 2.8
horizontal_aperture: 4.890881131191918
vertical_aperture: 2.7608816125932627
convention: opengl
attach_to:
target_name: r1pro_dex
is_articulation_part: true
articulation_part_name: zed_link
create_fixed_joint: true
local_position: [0.0, 0.0, 0.0]
local_rotation: [0.33, 1.0, -0.0, 0.0]
# Third-person view of the whole robot.
front_camera:
name: front_camera
stereotype: camera
position: [2.2, 0.0, 1.4]
look_at:
is_point: true
look_at_point: [0.0, 0.0, 1.0]
data_types: [rgb]
width: 1280
height: 720
camera_model: pinhole
fix_camera: true
light_cfg_dict:
sun:
name: sun
stereotype: general_light
light_type: distant
position: [0, 0, 5]
rotation: [1, 0, 0, 0]
intensity: 1000
angle: 0.53
color: [1.0, 1.0, 1.0]
sky:
name: sky
stereotype: general_light
light_type: dome
intensity: 10.0
color: [1.0, 1.0, 1.0]
extension:
extension_cfg_dict:
data_collect:
enable: true
stereotype: data_collect
observer_cfgs:
- stereotype: robot_observer
name: r1pro_dex
observe_joint_positions: true
observe_joint_velocities: true
observe_joint_position_targets: true
observe_ee_pose: true
- stereotype: sensor_observer
name: head_camera
observe_rgb: true
- stereotype: sensor_observer
name: front_camera
observe_rgb: true
record:
enable: true
stereotype: record
backend_root_path: output://benchmark_replay_record
data_collector_name: data_collect
record_backend: lfs
record_fps: 15 # matches the source dataset fps
use_recorder_step: true
record_scene: true
postprocess_list: [hdf5, video, preview_video]