Feature/add multitask diffusion transformer policy implementation (#2545)

* Add multitask diffusion transformer policy Add multitask diffusion transformer policy * expand the observation encoder to support differnt size encoders for vision and text * add RoPE attention module as this is shown to help training dynamics and generation quality for DiTs * update readme and citations for multitask dit policy * remove dino vision encoder and simplify text and vision encoders by removing inheritance structure * adjust factory comment * update docstring for multitask dit policy processor file * simplify config for multitask dit by merging and flattening everything, then adding comments to denote where some parameters are only used for specific objectives * add references to the modeling file comments * merge all modules files into the main modeling file * add torch.no_grad decorators * split up select action return statement * remove redundant asserts * add tutorial to training with multi_task_dit * fix bugs when testing on hardware * remove environment state conditioning * update typo in test instruction comment * add processor tests to multitask dit tests * move policy to top of file * use constants for indexing into batches and remove env state references * remove the base classes since we don't need to be able to extend * fix nit formatting in generate actions fcn * reformat and clean up tutorial for multitask dit policy * add more descriptions and depth to multitask dit tutorial * note origins of each training objective * rename config param for multiple vision encoders * refactor code to perform task tokenization in the processor instead of in the modeling code for multitask dit * add multitask dit to toc for docs * add conditional transformers import to match all other policies that use transformers lib * add test handling for multitask dit when transformers isnt available * skip tests without transformers * remove cropping of images smaller than the crop size * add kwargs arg to multitask dit constructor * add wallx dep conflict management for multitask dit policy * use hyphens for cleanliness in pyproject.toml * add conflict management to pyproject toml for pi conflict for mtdp as well * update tests script to not use unnecessary uv sync call which resolves dependencies that do not need to run. This drastically reduces CI run time * revert fast tests edits * update docs and readme files, fixing some typos and adding multitask dit to readme * chore(dependencies): upgrade transformers + hggingface-hub + peft + scipy * chore(dependencies): bump pi0 family to transformers v5 * chore(dependencies): bump wall x to transformers v5 * chore(dependencies): bump gr00t to transformers v5 * chore(style): fix pre-commit * fix(policy): xvla forced_bos_token missing * test(rl): skip ci tests for resnet10 * Fix: full pi models support for transformer v5 (#2967) * fix(pi): remove loss truncation * fix(pi): remove state padding before tokenization * fix(pi): fix image padding value * fix from_pretrain * add transformer v5 changes * remove reference * more fixes * make it work * add support for rest of pi family * add pifast work * more changes * more changes * more cleanup * fix torch params * dtype fix * torch compile * embed mismatch fix * revert groot * more nit fixes * remove unused classes * more fixes * revert * nit * torch dtype warning fix * but back dynamic renaming * add tie embedding --------- Co-authored-by: Yufei Sun <skieyfly@gmail.com> * chore: fix XVLA in transformers v5 (#3006) * test(policies): enable wall x CI testing * style(test): pre-commit check * style(test): pre-commit --------- Signed-off-by: Bryson Jones <63133702+brysonjones@users.noreply.github.com> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Jade Choghari <chogharijade@gmail.com> Co-authored-by: Yufei Sun <skieyfly@gmail.com> Co-authored-by: Steven Palma <steven.palma@huggingface.co>
2026-05-18 18:20:08 +00:00 · 2026-03-27 16:41:26 -07:00
parent 4e45acca52
commit 2e069b1c47
13 changed files with 2253 additions and 9 deletions
@@ -0,0 +1,624 @@
+#!/usr/bin/env python
+
+# Copyright 2025 Bryson Jones and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# ruff: noqa: E402
+
+"""Test script for Multi-Task DiT policy.
+
+To run tests locally:
+    python -m pytest tests/policies/multi_task_dit/test_multi_task_dit.py -v
+"""
+
+import os
+
+import pytest
+import torch
+from torch import Tensor
+
+pytest.importorskip("transformers")
+
+pytestmark = pytest.mark.skipif(
+    os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
+    reason="This test requires local transformers installation and is not meant for CI",
+)
+
+from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+from lerobot.policies.multi_task_dit.configuration_multi_task_dit import MultiTaskDiTConfig
+from lerobot.policies.multi_task_dit.modeling_multi_task_dit import MultiTaskDiTPolicy
+from lerobot.policies.multi_task_dit.processor_multi_task_dit import (
+    make_multi_task_dit_pre_post_processors,
+)
+from lerobot.utils.constants import (
+    ACTION,
+    OBS_IMAGES,
+    OBS_LANGUAGE_ATTENTION_MASK,
+    OBS_LANGUAGE_TOKENS,
+    OBS_STATE,
+)
+from lerobot.utils.random_utils import seeded_context, set_seed
+
+
+@pytest.fixture(autouse=True)
+def set_random_seed():
+    seed = 17
+    set_seed(seed)
+
+
+def create_train_batch(
+    batch_size: int = 2,
+    n_obs_steps: int = 2,
+    horizon: int = 16,
+    state_dim: int = 10,
+    action_dim: int = 10,
+    height: int = 224,
+    width: int = 224,
+) -> dict[str, Tensor]:
+    """Create a training batch with visual input and text."""
+    return {
+        "observation.state": torch.randn(batch_size, n_obs_steps, state_dim),
+        f"{OBS_IMAGES}.laptop": torch.rand(batch_size, n_obs_steps, 3, height, width),
+        ACTION: torch.randn(batch_size, horizon, action_dim),
+        "task": ["pick up the cube"] * batch_size,
+    }
+
+
+def create_observation_batch(
+    batch_size: int = 2, state_dim: int = 10, height: int = 224, width: int = 224
+) -> dict:
+    """Create observation batch for inference for a single timestep."""
+    return {
+        "observation.state": torch.randn(batch_size, state_dim),
+        f"{OBS_IMAGES}.laptop": torch.rand(batch_size, 3, height, width),
+        "task": ["pick up the red cube"] * batch_size,
+    }
+
+
+def create_config(
+    state_dim: int = 10,
+    action_dim: int = 10,
+    n_obs_steps: int = 2,
+    horizon: int = 16,
+    n_action_steps: int = 8,
+    with_visual: bool = True,
+    height: int = 224,
+    width: int = 224,
+) -> MultiTaskDiTConfig:
+    """Create a MultiTaskDiT config for testing.
+
+    Args:
+        state_dim: Dimension of state observations
+        action_dim: Dimension of actions
+        n_obs_steps: Number of observation steps
+        horizon: Action prediction horizon
+        n_action_steps: Number of action steps to execute
+        with_visual: Whether to include visual input (default: True)
+        height: Image height (only used if with_visual=True)
+        width: Image width (only used if with_visual=True)
+    """
+    input_features = {OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(state_dim,))}
+
+    if with_visual:
+        input_features[f"{OBS_IMAGES}.laptop"] = PolicyFeature(
+            type=FeatureType.VISUAL, shape=(3, height, width)
+        )
+
+    config = MultiTaskDiTConfig(
+        input_features=input_features,
+        output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,))},
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        n_action_steps=n_action_steps,
+        # Use smaller model for faster tests
+        hidden_dim=128,
+        num_layers=2,
+        num_heads=4,
+    )
+
+    config.validate_features()
+    return config
+
+
+@pytest.mark.parametrize("batch_size,state_dim,action_dim", [(2, 10, 10), (1, 6, 6)])
+def test_multi_task_dit_policy_forward(batch_size: int, state_dim: int, action_dim: int):
+    """Test forward pass (training mode)."""
+    n_obs_steps = 2
+    horizon = 16
+    n_action_steps = 8
+
+    config = create_config(
+        state_dim=state_dim,
+        action_dim=action_dim,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        n_action_steps=n_action_steps,
+    )
+
+    policy = MultiTaskDiTPolicy(config=config)
+    policy.train()
+
+    # Use preprocessor to handle tokenization
+    config.normalization_mapping = {
+        "VISUAL": NormalizationMode.IDENTITY,
+        "STATE": NormalizationMode.IDENTITY,
+        "ACTION": NormalizationMode.IDENTITY,
+    }
+    preprocessor, _ = make_multi_task_dit_pre_post_processors(config=config, dataset_stats=None)
+
+    batch = create_train_batch(
+        batch_size=batch_size,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        state_dim=state_dim,
+        action_dim=action_dim,
+    )
+
+    # Process batch through preprocessor to tokenize task text
+    processed_batch = preprocessor(batch)
+
+    # Test forward pass
+    loss, _ = policy.forward(processed_batch)
+    assert loss is not None
+    assert loss.item() is not None
+    assert loss.shape == ()
+
+    # Test backward pass
+    loss.backward()
+
+
+def test_multi_task_dit_pre_post_processors():
+    """Test pre and post processors for Multi-Task DiT policy."""
+    state_dim = 10
+    action_dim = 8
+    n_obs_steps = 2
+    horizon = 16
+
+    config = create_config(
+        state_dim=state_dim,
+        action_dim=action_dim,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        n_action_steps=8,
+    )
+    config.device = "cpu"
+
+    # Set normalization mode to match the stats we're providing
+    config.normalization_mapping = {
+        "VISUAL": NormalizationMode.IDENTITY,
+        "STATE": NormalizationMode.MEAN_STD,  # Use MEAN_STD since we provide mean/std stats
+        "ACTION": NormalizationMode.MIN_MAX,
+    }
+
+    # Create dataset stats for normalization
+    dataset_stats = {
+        "observation.state": {
+            "mean": torch.zeros(state_dim),
+            "std": torch.ones(state_dim),
+        },
+        "action": {
+            "min": torch.full((action_dim,), -1.0),
+            "max": torch.ones(action_dim),
+        },
+    }
+
+    # Create processors
+    preprocessor, postprocessor = make_multi_task_dit_pre_post_processors(
+        config=config, dataset_stats=dataset_stats
+    )
+
+    # Test preprocessor with sample data
+    batch = {
+        "observation.state": torch.randn(state_dim),
+        f"{OBS_IMAGES}.laptop": torch.rand(3, 224, 224),
+        ACTION: torch.randn(action_dim),
+        "task": "pick up the cube",
+    }
+
+    processed_batch = preprocessor(batch)
+
+    # Check that data is batched
+    assert processed_batch["observation.state"].shape == (1, state_dim)
+    assert processed_batch[f"{OBS_IMAGES}.laptop"].shape == (1, 3, 224, 224)
+    assert processed_batch[ACTION].shape == (1, action_dim)
+    # Check that task text was tokenized
+    assert OBS_LANGUAGE_TOKENS in processed_batch
+    assert OBS_LANGUAGE_ATTENTION_MASK in processed_batch
+    assert processed_batch[OBS_LANGUAGE_TOKENS].shape[0] == 1  # batch dimension
+    assert processed_batch[OBS_LANGUAGE_ATTENTION_MASK].shape[0] == 1  # batch dimension
+
+    # Check that data is on correct device
+    assert processed_batch["observation.state"].device.type == "cpu"
+    assert processed_batch[f"{OBS_IMAGES}.laptop"].device.type == "cpu"
+    assert processed_batch[ACTION].device.type == "cpu"
+
+    # Test postprocessor with sample action (PolicyAction is just a torch.Tensor)
+    action = torch.randn(1, action_dim)
+    processed_action = postprocessor(action)
+
+    # Check that action is unnormalized and on CPU
+    assert processed_action.shape == (1, action_dim)
+    assert processed_action.device.type == "cpu"
+
+
+def test_multi_task_dit_pre_post_processors_normalization():
+    """Test that normalization and unnormalization work correctly with simple sanity check numbers."""
+    state_dim = 3
+    action_dim = 2
+
+    config = create_config(
+        state_dim=state_dim,
+        action_dim=action_dim,
+        n_obs_steps=2,
+        horizon=16,
+        n_action_steps=8,
+    )
+    config.device = "cpu"
+
+    # Set normalization mode to match the stats we're providing
+    config.normalization_mapping = {
+        "VISUAL": NormalizationMode.IDENTITY,
+        "STATE": NormalizationMode.MEAN_STD,  # Use MEAN_STD since we provide mean/std stats
+        "ACTION": NormalizationMode.MIN_MAX,
+    }
+
+    # Use simple stats that will actually transform the values
+    dataset_stats = {
+        "observation.state": {
+            "mean": torch.full((state_dim,), 5.0),
+            "std": torch.full((state_dim,), 2.0),
+        },
+        "action": {
+            "min": torch.zeros(action_dim),
+            "max": torch.full((action_dim,), 2.0),
+        },
+    }
+
+    # Create processors
+    preprocessor, postprocessor = make_multi_task_dit_pre_post_processors(
+        config=config, dataset_stats=dataset_stats
+    )
+
+    # Use simple input values
+    input_state = torch.tensor([7.0, 5.0, 3.0])  # Will normalize to [1.0, 0.0, -1.0]
+    input_action = torch.tensor([1.0, 2.0])  # Will normalize to [0.0, 1.0]
+
+    batch = {
+        "observation.state": input_state,
+        f"{OBS_IMAGES}.laptop": torch.rand(3, 224, 224),
+        ACTION: input_action,
+        "task": "test task",
+    }
+
+    # Process through preprocessor
+    processed_batch = preprocessor(batch)
+
+    # State normalization: (x - mean) / std
+    expected_normalized_state = torch.tensor([1.0, 0.0, -1.0])
+    assert torch.allclose(processed_batch["observation.state"][0], expected_normalized_state, atol=1e-5)
+
+    # Action normalization: (x - min) / (max - min) * 2 - 1
+    expected_normalized_action = torch.tensor([0.0, 1.0])
+    assert torch.allclose(processed_batch[ACTION][0], expected_normalized_action, atol=1e-5)
+
+    # Test unnormalization: should recover original values
+    normalized_action_tensor = processed_batch[ACTION][0:1]  # Keep batch dimension
+    unnormalized_action = postprocessor(normalized_action_tensor)
+
+    # Should recover original action values
+    assert torch.allclose(unnormalized_action[0], input_action, atol=1e-4)
+
+
+@pytest.mark.parametrize("batch_size,state_dim,action_dim", [(2, 10, 10), (1, 6, 6)])
+def test_multi_task_dit_policy_select_action(batch_size: int, state_dim: int, action_dim: int):
+    """Test select_action (inference mode)."""
+    n_obs_steps = 2
+    horizon = 16
+    n_action_steps = 8
+
+    config = create_config(
+        state_dim=state_dim,
+        action_dim=action_dim,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        n_action_steps=n_action_steps,
+    )
+
+    policy = MultiTaskDiTPolicy(config=config)
+    policy.eval()
+    policy.reset()  # Reset queues before inference
+
+    # Create processors - use IDENTITY normalization when no stats provided
+    config.normalization_mapping = {
+        "VISUAL": NormalizationMode.IDENTITY,
+        "STATE": NormalizationMode.IDENTITY,
+        "ACTION": NormalizationMode.IDENTITY,
+    }
+    preprocessor, postprocessor = make_multi_task_dit_pre_post_processors(config=config, dataset_stats=None)
+
+    with torch.no_grad():
+        observation_batch = create_observation_batch(batch_size=batch_size, state_dim=state_dim)
+        # Process observation through preprocessor
+        processed_obs = preprocessor(observation_batch)
+        selected_action = policy.select_action(processed_obs)
+        # Process action through postprocessor (PolicyAction is just a torch.Tensor)
+        processed_action = postprocessor(selected_action)
+        assert processed_action.shape == (batch_size, action_dim)
+
+
+def test_multi_task_dit_policy_diffusion_objective():
+    """Test policy with diffusion objective."""
+    batch_size = 2
+    state_dim = 10
+    action_dim = 10
+    n_obs_steps = 2
+    horizon = 16
+    n_action_steps = 8
+
+    input_features = {
+        OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(state_dim,)),
+        f"{OBS_IMAGES}.laptop": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)),
+    }
+
+    config = MultiTaskDiTConfig(
+        input_features=input_features,
+        output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,))},
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        n_action_steps=n_action_steps,
+        # Use diffusion objective
+        objective="diffusion",
+        noise_scheduler_type="DDPM",
+        num_train_timesteps=100,
+        num_inference_steps=10,
+        # Smaller model for tests
+        hidden_dim=128,
+        num_layers=2,
+        num_heads=4,
+    )
+    config.validate_features()
+
+    policy = MultiTaskDiTPolicy(config=config)
+    policy.train()
+
+    # Use preprocessor to handle tokenization
+    config.normalization_mapping = {
+        "VISUAL": NormalizationMode.IDENTITY,
+        "STATE": NormalizationMode.IDENTITY,
+        "ACTION": NormalizationMode.IDENTITY,
+    }
+    preprocessor, _ = make_multi_task_dit_pre_post_processors(config=config, dataset_stats=None)
+
+    batch = create_train_batch(
+        batch_size=batch_size,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        state_dim=state_dim,
+        action_dim=action_dim,
+    )
+
+    # Process batch through preprocessor to tokenize task text
+    processed_batch = preprocessor(batch)
+
+    # Test forward pass
+    loss, _ = policy.forward(processed_batch)
+    assert loss is not None
+    assert loss.item() is not None
+
+    # Test inference
+    policy.eval()
+    # Use IDENTITY normalization when no stats provided
+    config.normalization_mapping = {
+        "VISUAL": NormalizationMode.IDENTITY,
+        "STATE": NormalizationMode.IDENTITY,
+        "ACTION": NormalizationMode.IDENTITY,
+    }
+    preprocessor, postprocessor = make_multi_task_dit_pre_post_processors(config=config, dataset_stats=None)
+    with torch.no_grad():
+        observation_batch = create_observation_batch(batch_size=batch_size, state_dim=state_dim)
+        # Process observation through preprocessor
+        processed_obs = preprocessor(observation_batch)
+        selected_action = policy.select_action(processed_obs)
+        # Process action through postprocessor (PolicyAction is just a torch.Tensor)
+        processed_action = postprocessor(selected_action)
+        assert processed_action.shape == (batch_size, action_dim)
+
+
+def test_multi_task_dit_policy_flow_matching_objective():
+    """Test policy with flow matching objective."""
+    batch_size = 2
+    state_dim = 10
+    action_dim = 10
+    n_obs_steps = 2
+    horizon = 16
+    n_action_steps = 8
+
+    input_features = {
+        OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(state_dim,)),
+        f"{OBS_IMAGES}.laptop": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)),
+    }
+
+    config = MultiTaskDiTConfig(
+        input_features=input_features,
+        output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,))},
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        n_action_steps=n_action_steps,
+        # Use flow matching objective
+        objective="flow_matching",
+        sigma_min=0.0,
+        num_integration_steps=10,  # Fewer steps for faster tests
+        integration_method="euler",
+        # Smaller model for tests
+        hidden_dim=128,
+        num_layers=2,
+        num_heads=4,
+    )
+    config.validate_features()
+
+    policy = MultiTaskDiTPolicy(config=config)
+    policy.train()
+
+    # Use preprocessor to handle tokenization
+    config.normalization_mapping = {
+        "VISUAL": NormalizationMode.IDENTITY,
+        "STATE": NormalizationMode.IDENTITY,
+        "ACTION": NormalizationMode.IDENTITY,
+    }
+    preprocessor, _ = make_multi_task_dit_pre_post_processors(config=config, dataset_stats=None)
+
+    batch = create_train_batch(
+        batch_size=batch_size,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        state_dim=state_dim,
+        action_dim=action_dim,
+    )
+
+    # Process batch through preprocessor to tokenize task text
+    processed_batch = preprocessor(batch)
+
+    # Test forward pass
+    loss, _ = policy.forward(processed_batch)
+    assert loss is not None
+    assert loss.item() is not None
+
+    # Test inference
+    policy.eval()
+    # Use IDENTITY normalization when no stats provided
+    config.normalization_mapping = {
+        "VISUAL": NormalizationMode.IDENTITY,
+        "STATE": NormalizationMode.IDENTITY,
+        "ACTION": NormalizationMode.IDENTITY,
+    }
+    preprocessor, postprocessor = make_multi_task_dit_pre_post_processors(config=config, dataset_stats=None)
+    with torch.no_grad():
+        observation_batch = create_observation_batch(batch_size=batch_size, state_dim=state_dim)
+        # Process observation through preprocessor
+        processed_obs = preprocessor(observation_batch)
+        selected_action = policy.select_action(processed_obs)
+        # Process action through postprocessor (PolicyAction is just a torch.Tensor)
+        processed_action = postprocessor(selected_action)
+        assert processed_action.shape == (batch_size, action_dim)
+
+
+def test_multi_task_dit_policy_save_and_load(tmp_path):
+    """Test that the policy can be saved and loaded correctly."""
+    root = tmp_path / "test_multi_task_dit_save_and_load"
+
+    state_dim = 10
+    action_dim = 10
+    batch_size = 2
+    n_obs_steps = 2
+    horizon = 16
+    n_action_steps = 8
+
+    config = create_config(
+        state_dim=state_dim,
+        action_dim=action_dim,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        n_action_steps=n_action_steps,
+    )
+
+    policy = MultiTaskDiTPolicy(config=config)
+    policy.eval()
+
+    # Get device before saving
+    device = next(policy.parameters()).device
+
+    policy.save_pretrained(root)
+    loaded_policy = MultiTaskDiTPolicy.from_pretrained(root, config=config)
+
+    # Explicitly move loaded_policy to the same device
+    loaded_policy.to(device)
+    loaded_policy.eval()
+
+    batch = create_train_batch(
+        batch_size=batch_size,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        state_dim=state_dim,
+        action_dim=action_dim,
+    )
+
+    # Use preprocessor to handle tokenization
+    config.normalization_mapping = {
+        "VISUAL": NormalizationMode.IDENTITY,
+        "STATE": NormalizationMode.IDENTITY,
+        "ACTION": NormalizationMode.IDENTITY,
+    }
+    preprocessor, postprocessor = make_multi_task_dit_pre_post_processors(config=config, dataset_stats=None)
+
+    with torch.no_grad():
+        with seeded_context(12):
+            # Process batch through preprocessor
+            processed_batch = preprocessor(batch)
+            # Move batch to the same device as the policy
+            for key in processed_batch:
+                if isinstance(processed_batch[key], torch.Tensor):
+                    processed_batch[key] = processed_batch[key].to(device)
+            # Collect policy values before saving
+            loss, _ = policy.forward(processed_batch)
+
+            observation_batch = create_observation_batch(batch_size=batch_size, state_dim=state_dim)
+            # Process observation through preprocessor
+            processed_obs = preprocessor(observation_batch)
+            actions = policy.select_action(processed_obs)
+
+        with seeded_context(12):
+            # Process batch through preprocessor
+            processed_batch = preprocessor(batch)
+            # Collect policy values after loading
+            loaded_loss, _ = loaded_policy.forward(processed_batch)
+
+            loaded_observation_batch = create_observation_batch(batch_size=batch_size, state_dim=state_dim)
+            processed_obs = preprocessor(loaded_observation_batch)
+            loaded_actions = loaded_policy.select_action(processed_obs)
+
+        # Compare state dicts
+        assert policy.state_dict().keys() == loaded_policy.state_dict().keys()
+        for k in policy.state_dict():
+            assert torch.allclose(policy.state_dict()[k], loaded_policy.state_dict()[k], atol=1e-6)
+
+        # Compare values before and after saving and loading
+        assert torch.allclose(loss, loaded_loss)
+        assert torch.allclose(actions, loaded_actions)
+
+
+def test_multi_task_dit_policy_get_optim_params():
+    """Test that the policy returns correct optimizer parameter groups."""
+    config = create_config(
+        state_dim=10,
+        action_dim=10,
+        n_obs_steps=2,
+        horizon=16,
+        n_action_steps=8,
+    )
+
+    policy = MultiTaskDiTPolicy(config=config)
+    param_groups = policy.get_optim_params()
+
+    # Should have 2 parameter groups: non-vision and vision encoder
+    assert len(param_groups) == 2
+
+    # First group is non-vision params (no lr specified, will use default)
+    assert "params" in param_groups[0]
+    assert len(param_groups[0]["params"]) > 0
+
+    # Second group is vision encoder params with different lr
+    assert "params" in param_groups[1]
+    assert "lr" in param_groups[1]
+    expected_lr = config.optimizer_lr * config.vision_encoder_lr_multiplier
+    assert param_groups[1]["lr"] == expected_lr