File size: 3,315 Bytes
46bb2e5 | 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 | """Minimal loader for openroboto-ai/axis-franka-datapool.
Install with: pip install "lerobot>=0.4" torch numpy
"""
from __future__ import annotations
import numpy as np
import torch
from torch.utils.data import Dataset
FINGER_OPEN = 0.04
PI05_DT = 1.0 / 15.0
def _closedness(fingers: torch.Tensor) -> torch.Tensor:
return torch.clamp(1.0 - fingers.mean(dim=-1) / FINGER_OPEN, 0.0, 1.0)
def to_pi05_droid(state: torch.Tensor, action: torch.Tensor):
"""Convert raw 9D qpos/qpos-target into Pi0.5-Axis's 8D DROID view."""
if state.shape[-1] != 9 or action.shape[-1] != 9:
raise ValueError(f"Expected 9D state/action, got {state.shape}/{action.shape}")
converted_state = torch.cat([state[..., 2:9], _closedness(state[..., :2])[..., None]], dim=-1)
arm_action = torch.clamp((action[..., 2:9] - state[..., 2:9]) / PI05_DT, -1.0, 1.0)
converted_action = torch.cat([arm_action, _closedness(action[..., :2])[..., None]], dim=-1)
return converted_state, converted_action
class AxisFrankaDataset(Dataset):
"""LeRobot-backed dataset with explicit raw-joint or Pi0.5/DROID action view."""
def __init__(
self,
repo_id="openroboto-ai/axis-franka-datapool",
*,
revision="v0.1-metadata",
action_space="joint_position_target",
sample_hz=30,
root=None,
):
from lerobot.datasets.lerobot_dataset import LeRobotDataset
if action_space not in {"joint_position_target", "pi05_droid"}:
raise ValueError("action_space must be 'joint_position_target' or 'pi05_droid'")
if sample_hz not in {15, 30}:
raise ValueError("sample_hz must be 15 or 30")
self.raw = LeRobotDataset(
repo_id=repo_id,
revision=revision,
root=root,
download_videos=False,
)
self.action_space = action_space
if sample_hz == 30:
self.indices = None
else:
# AXIS was recorded at 30 Hz. Taking even frame indices within
# each episode exactly reproduces the official 2x temporal stride.
# Use the compact episode table rather than formatting/scanning
# all 2.8M frame_index values through the torch dataset transform.
episodes = self.raw.meta.episodes
starts = np.asarray(episodes["dataset_from_index"], dtype=np.int64)
stops = np.asarray(episodes["dataset_to_index"], dtype=np.int64)
self.indices = np.concatenate(
[np.arange(start, stop, 2, dtype=np.int64) for start, stop in zip(starts, stops)]
)
def __len__(self):
return len(self.raw) if self.indices is None else len(self.indices)
def __getitem__(self, index):
raw_index = int(index) if self.indices is None else int(self.indices[index])
sample = self.raw[raw_index]
if self.action_space == "joint_position_target":
return sample
result = dict(sample)
result["observation.state"], result["action"] = to_pi05_droid(
sample["observation.state"], sample["action"]
)
return result
def load_dataset(**kwargs):
"""Convenience entry point; keyword arguments are passed to AxisFrankaDataset."""
return AxisFrankaDataset(**kwargs)
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