File size: 4,115 Bytes
e0803b8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | #!/usr/bin/env python3
"""Convert one BONES-SEED Unitree G1 CSV file to MT NPZ format."""
from __future__ import annotations
import argparse
from pathlib import Path
import mujoco
import numpy as np
from scipy.spatial.transform import Rotation as R
from converter_common import (
MT_BODY_NAMES,
MT_JOINT_NAMES,
ensure_output_parent,
interpolate_linear,
interpolate_quaternions,
load_csv_motion,
load_g1_model,
output_frame_count,
require_body_id,
validate_fps,
)
MIMIC_BODY_SOURCES = {
"head_mimic": "torso_link",
"left_hand_mimic": "left_wrist_roll_link",
"right_hand_mimic": "right_wrist_roll_link",
}
def convert_csv_to_mt(
csv_path: str | Path,
output_path: str | Path,
g1_xml: str | Path,
source_fps: int = 120,
target_fps: int = 50,
verbose: bool = True,
) -> Path:
"""Convert a single CSV motion to MT format and return the output path."""
validate_fps(source_fps, target_fps)
root_pos, root_rot_xyzw, dof_pos = load_csv_motion(csv_path)
model = load_g1_model(str(Path(g1_xml).expanduser().resolve()))
model_data = mujoco.MjData(model)
physical_body_names = [
name for name in MT_BODY_NAMES if name not in MIMIC_BODY_SOURCES
]
body_ids = {
name: require_body_id(model, name) for name in physical_body_names
}
body_pos_w = np.zeros(
(len(root_pos), len(MT_BODY_NAMES), 3), dtype=np.float32
)
for frame in range(len(root_pos)):
model_data.qpos[:3] = root_pos[frame]
model_data.qpos[3:7] = root_rot_xyzw[frame, [3, 0, 1, 2]]
model_data.qpos[7:] = dof_pos[frame]
mujoco.mj_forward(model, model_data)
for body_name, body_id in body_ids.items():
output_index = MT_BODY_NAMES.index(body_name)
body_pos_w[frame, output_index] = model_data.xpos[body_id]
for mimic_name, source_name in MIMIC_BODY_SOURCES.items():
body_pos_w[:, MT_BODY_NAMES.index(mimic_name)] = body_pos_w[
:, MT_BODY_NAMES.index(source_name)
]
root_rotation_inverse = R.from_quat(root_rot_xyzw).inv().as_matrix()
local_body_pos = np.einsum(
"tij,tbj->tbi",
root_rotation_inverse,
body_pos_w - root_pos[:, None, :],
).astype(np.float32)
target_frames = output_frame_count(
len(root_pos), source_fps, target_fps
)
root_pos = interpolate_linear(root_pos, target_frames)
root_rot_xyzw = interpolate_quaternions(
root_rot_xyzw, target_frames
)
dof_pos = interpolate_linear(dof_pos, target_frames)
local_body_pos = interpolate_linear(local_body_pos, target_frames)
output_path = ensure_output_parent(output_path)
np.savez(
output_path,
root_pos=root_pos.astype(np.float32),
root_rot=root_rot_xyzw.astype(np.float32),
dof_pos=dof_pos.astype(np.float32),
local_body_pos=local_body_pos.astype(np.float32),
joint_names=np.asarray(MT_JOINT_NAMES),
body_names=np.asarray(MT_BODY_NAMES),
fps=np.asarray([target_fps], dtype=np.int64),
)
if verbose:
print(
f"Saved MT: {output_path} "
f"({len(root_pos)} frames @ {target_fps} fps)"
)
return output_path
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Convert one BONES-SEED G1 CSV motion to MT NPZ"
)
parser.add_argument("--input", required=True, help="Input G1 CSV file")
parser.add_argument("--output", required=True, help="Output MT NPZ file")
parser.add_argument(
"--g1-xml", required=True, help="Compatible Unitree G1 MJCF XML"
)
parser.add_argument("--source-fps", type=int, default=120)
parser.add_argument("--target-fps", type=int, default=50)
return parser.parse_args()
def main() -> None:
args = parse_args()
convert_csv_to_mt(
csv_path=args.input,
output_path=args.output,
g1_xml=args.g1_xml,
source_fps=args.source_fps,
target_fps=args.target_fps,
)
if __name__ == "__main__":
main()
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