mirror of
https://github.com/huggingface/lerobot.git
synced 2026-07-14 05:22:14 +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>
79 lines
2.6 KiB
Python
79 lines
2.6 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 pytest
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from lerobot.configs.policies import PreTrainedConfig
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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.utils.constants import ACTION, OBS_IMAGES
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def make_config(**overrides) -> LingBotVAConfig:
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kwargs = {"device": "cpu"}
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kwargs.update(overrides)
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return LingBotVAConfig(**kwargs)
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def test_registered_in_choice_registry() -> None:
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assert "lingbot_va" in PreTrainedConfig.get_known_choices()
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assert PreTrainedConfig.get_choice_class("lingbot_va") is LingBotVAConfig
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def test_type_property() -> None:
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assert make_config().type == "lingbot_va"
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def test_chunk_size_and_action_steps() -> None:
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cfg = make_config(frame_chunk_size=4, action_per_frame=4)
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assert cfg.chunk_size == 16
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assert cfg.n_action_steps == 16
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assert cfg.action_delta_indices == list(range(16))
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assert cfg.observation_delta_indices is None
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assert cfg.reward_delta_indices is None
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def test_optimizer_and_scheduler_presets() -> None:
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cfg = make_config()
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opt = cfg.get_optimizer_preset()
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assert opt.lr == cfg.optimizer_lr
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sched = cfg.get_scheduler_preset()
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assert sched.num_warmup_steps == cfg.scheduler_warmup_steps
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def test_validate_features_sets_action_feature() -> None:
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cfg = make_config()
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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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assert ACTION in cfg.output_features
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assert cfg.output_features[ACTION].shape == (len(cfg.used_action_channel_ids),)
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def test_validate_features_no_visual_raises() -> None:
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cfg = make_config()
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cfg.input_features = {}
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cfg.output_features = {}
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with pytest.raises(ValueError, match="at least one visual input feature"):
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cfg.validate_features()
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def test_invalid_attn_mode_raises() -> None:
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with pytest.raises(ValueError, match="attn_mode"):
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make_config(attn_mode="banana")
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