mirror of
https://github.com/huggingface/lerobot.git
synced 2026-07-08 18:41:54 +00:00
pepijn223
b81909fc28
Make the LingBot-VA port runnable on both LIBERO and RoboTwin and clean up the package to LeRobot conventions. - Consolidate all vendored Wan2.2 model code (transformer, attention, VAE helpers, flow-matching scheduler, grid utils, flex-attention) into a single modeling_lingbot_va.py; remove the separate wan_*/schedulers modules. - Move the fixed action (un)normalization quantiles out of the config and into the post-processor (LIBERO 7-DoF + RoboTwin 16-d eef); remove the conversion script in favour of ready-to-use LeRobot-format checkpoints on the Hub. - Fixes found via on-sim validation: undo LIBERO's 180-degree image flip (image_hflip), encode obs as a multi-frame streaming-VAE clip, reset the streaming VAE cache between episodes, run the transformer in config.dtype, lazy-load frozen VAE/UMT5 by subfolder with the text encoder on CPU. - RoboTwin: add an end-effector-pose action mode to RoboTwinEnv (16-d per-arm xyz+quat+gripper deltas composed onto the initial eef pose, executed via CuRobo IK) and the robotwin_tshape latent layout (full-res head + half-res wrists via a second streaming VAE) with the upstream RoboTwin action quantiles + camera mapping. - Predicted-video saving works for both benchmarks; docs + tests updated. Co-authored-by: Cursor <cursoragent@cursor.com>
83 lines
3.1 KiB
Python
83 lines
3.1 KiB
Python
#!/usr/bin/env python
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# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import torch
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from lerobot.configs.types import FeatureType, PolicyFeature
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from lerobot.policies.lingbot_va.configuration_lingbot_va import LingBotVAConfig
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from lerobot.policies.lingbot_va.processor_lingbot_va import (
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LIBERO_ACTION_Q01,
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LingBotVAActionUnnormalizeStep,
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make_lingbot_va_pre_post_processors,
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)
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from lerobot.utils.constants import (
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OBS_IMAGES,
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POLICY_POSTPROCESSOR_DEFAULT_NAME,
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POLICY_PREPROCESSOR_DEFAULT_NAME,
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)
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def _make_config() -> LingBotVAConfig:
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cfg = LingBotVAConfig(device="cpu")
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cfg.input_features = {f"{OBS_IMAGES}.image": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 128, 128))}
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cfg.output_features = {}
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cfg.validate_features()
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return cfg
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def test_action_unnormalize_inverts_quantile_norm() -> None:
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q01 = [-1.0, -0.5, 0.0]
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q99 = [1.0, 0.5, 2.0]
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step = LingBotVAActionUnnormalizeStep(action_q01=q01, action_q99=q99)
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# Forward (the policy-side) quantile normalization: (x - q01) / (q99 - q01 + eps) * 2 - 1.
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q01_t = torch.tensor(q01)
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q99_t = torch.tensor(q99)
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raw = torch.tensor([[0.3, 0.1, 1.0]])
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normed = (raw - q01_t) / (q99_t - q01_t + 1e-6) * 2.0 - 1.0
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recovered = step.action(normed)
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assert torch.allclose(recovered, raw, atol=1e-4)
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def test_action_unnormalize_config_roundtrip() -> None:
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step = LingBotVAActionUnnormalizeStep(action_q01=[0.0, 1.0], action_q99=[2.0, 3.0])
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cfg = step.get_config()
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assert cfg == {"action_q01": [0.0, 1.0], "action_q99": [2.0, 3.0]}
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rebuilt = LingBotVAActionUnnormalizeStep(**cfg)
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assert rebuilt.action_q01 == step.action_q01
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assert rebuilt.action_q99 == step.action_q99
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def test_make_pre_post_processors_names_and_steps() -> None:
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cfg = _make_config()
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pre, post = make_lingbot_va_pre_post_processors(cfg, dataset_stats=None)
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assert pre.name == POLICY_PREPROCESSOR_DEFAULT_NAME
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assert post.name == POLICY_POSTPROCESSOR_DEFAULT_NAME
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# The postprocessor must contain the dedicated quantile unnormalize step.
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assert any(isinstance(s, LingBotVAActionUnnormalizeStep) for s in post.steps)
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def test_postprocessor_applies_unnormalization() -> None:
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cfg = _make_config()
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_, post = make_lingbot_va_pre_post_processors(cfg, dataset_stats=None)
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# A normalized action of all -1 should map back to q01 (the LIBERO 7-DoF default quantiles).
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normed = torch.full((1, len(cfg.used_action_channel_ids)), -1.0)
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out = post(normed)
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assert torch.allclose(out, torch.tensor(LIBERO_ACTION_Q01).unsqueeze(0), atol=1e-4)
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