156 lines
5.9 KiB
Python
156 lines
5.9 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from pathlib import Path
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import h5py
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import numpy as np
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@dataclass(frozen=True)
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class EpisodeTrajectory:
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source_path: Path
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times_s: np.ndarray
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target_poses: np.ndarray
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initial_joints: np.ndarray
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def _normalized_quaternion(values: np.ndarray) -> np.ndarray:
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quaternion = np.asarray(values, dtype=float)
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if quaternion.shape != (4,) or not np.isfinite(quaternion).all():
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raise ValueError("quaternion must contain 4 finite values")
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norm = float(np.linalg.norm(quaternion))
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if norm <= 1e-12:
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raise ValueError("quaternion norm must be positive")
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return quaternion / norm
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def _slerp_quaternion(
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start: np.ndarray,
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end: np.ndarray,
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fraction: float,
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) -> np.ndarray:
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first = _normalized_quaternion(start)
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second = _normalized_quaternion(end)
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dot = float(np.dot(first, second))
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if dot < 0.0:
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second = -second
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dot = -dot
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dot = float(np.clip(dot, -1.0, 1.0))
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if dot > 1.0 - 1e-8:
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return _normalized_quaternion(
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first + float(fraction) * (second - first)
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)
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angle = float(np.arccos(dot))
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sine = float(np.sin(angle))
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return _normalized_quaternion(
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np.sin((1.0 - fraction) * angle) / sine * first
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+ np.sin(fraction * angle) / sine * second
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)
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def resample_trajectory(
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trajectory: EpisodeTrajectory,
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sample_rate_hz: float,
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) -> EpisodeTrajectory:
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if not np.isfinite(sample_rate_hz) or sample_rate_hz <= 0.0:
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raise ValueError("sample_rate_hz must be finite and positive")
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source_times = np.asarray(trajectory.times_s, dtype=float)
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poses = np.asarray(trajectory.target_poses, dtype=float)
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if source_times.ndim != 1 or poses.shape != (source_times.size, 7):
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raise ValueError("trajectory must contain N timestamps and N x 7 poses")
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if source_times.size < 2 or np.any(np.diff(source_times) <= 0.0):
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raise ValueError("trajectory timestamps must be strictly increasing")
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duration = float(source_times[-1] - source_times[0])
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count = int(round(duration * sample_rate_hz)) + 1
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target_times = np.linspace(source_times[0], source_times[-1], count)
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target_poses = np.empty((count, 7), dtype=float)
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for axis in range(3):
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target_poses[:, axis] = np.interp(
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target_times,
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source_times,
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poses[:, axis],
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)
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for index, timestamp in enumerate(target_times):
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right = int(np.searchsorted(source_times, timestamp, side="right"))
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right = min(max(right, 1), source_times.size - 1)
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left = right - 1
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interval = source_times[right] - source_times[left]
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fraction = float((timestamp - source_times[left]) / interval)
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target_poses[index, 3:] = _slerp_quaternion(
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poses[left, 3:], poses[right, 3:], fraction
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)
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target_poses[0] = poses[0]
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target_poses[-1] = poses[-1]
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return EpisodeTrajectory(
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source_path=trajectory.source_path,
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times_s=target_times - target_times[0],
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target_poses=target_poses,
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initial_joints=np.asarray(trajectory.initial_joints, dtype=float).copy(),
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)
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def _longest_true_run(mask: np.ndarray) -> slice:
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values = np.asarray(mask, dtype=bool)
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best_start = best_stop = start = 0
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for index, enabled in enumerate(np.r_[values, False]):
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if enabled:
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continue
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if index - start > best_stop - best_start:
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best_start, best_stop = start, index
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start = index + 1
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if best_stop - best_start < 2:
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raise ValueError("episode has no valid teleoperation run")
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return slice(best_start, best_stop)
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def load_episode(path: Path) -> EpisodeTrajectory:
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source = Path(path).expanduser().resolve()
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if not source.is_file() or source.suffix.lower() not in (".h5", ".hdf5"):
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raise FileNotFoundError(f"episode not found: {source}")
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with h5py.File(source, "r") as handle:
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if str(handle.attrs.get("arm", "")) != "right_rm75":
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raise ValueError("episode arm must be right_rm75")
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if str(handle.attrs.get("pose_order", "")) != "x,y,z,qx,qy,qz,qw":
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raise ValueError("episode pose_order is unsupported")
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required = (
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"debug/tcp/final_target_pose",
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"debug/timestamps/control_monotonic_ns",
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"debug/control/teleop_active",
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"debug/control/action_valid",
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"debug/control/command_sent",
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"observations/qpos",
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)
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missing = [name for name in required if name not in handle]
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if missing:
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raise ValueError(f"episode datasets missing: {missing}")
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poses = np.asarray(handle[required[0]], dtype=float)
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timestamps = np.asarray(handle[required[1]], dtype=np.int64)
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mask = np.logical_and.reduce(
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[
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np.asarray(handle[required[2]], dtype=bool),
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np.asarray(handle[required[3]], dtype=bool),
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np.asarray(handle[required[4]], dtype=bool),
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]
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)
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qpos = np.asarray(handle[required[5]], dtype=float)
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if poses.shape != (timestamps.size, 7) or qpos.shape != (timestamps.size, 8):
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raise ValueError("episode arrays have inconsistent shapes")
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if not np.isfinite(poses).all() or not np.isfinite(qpos).all():
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raise ValueError("episode contains NaN/Inf")
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selected = _longest_true_run(mask)
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selected_times = timestamps[selected]
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if np.any(np.diff(selected_times) <= 0):
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raise ValueError("episode timestamps must be strictly increasing")
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selected_poses = poses[selected].copy()
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selected_poses[:, 3:] = np.asarray(
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[_normalized_quaternion(value) for value in selected_poses[:, 3:]]
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)
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return EpisodeTrajectory(
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source_path=source,
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times_s=(selected_times - selected_times[0]) * 1e-9,
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target_poses=selected_poses,
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initial_joints=qpos[selected.start, :7].copy(),
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)
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