refactor(tests): streamline transition creation in processor tests

- Replaced custom transition creation functions with a centralized `create_transition` function imported from converters across multiple test files.
- Updated test cases to utilize keyword arguments for better readability and maintainability, ensuring consistent transition creation throughout the test suite.

tests/processor/test_batch_conversion.py

@@ -1,3 +1,5 @@
+from typing import Any
+
 import torch
 
 from lerobot.processor import DataProcessorPipeline, TransitionKey
@@ -20,7 +22,7 @@ def _dummy_batch():
 
 def test_observation_grouping_roundtrip():
     """Test that observation.* keys are properly grouped and ungrouped."""
-    proc = DataProcessorPipeline([])
+    proc = DataProcessorPipeline[dict[str, Any]]([])
     batch_in = _dummy_batch()
     batch_out = proc(batch_in)
 
@@ -45,11 +47,12 @@ def test_observation_grouping_roundtrip():
 
 def test_batch_to_transition_observation_grouping():
     """Test that batch_to_transition correctly groups observation.* keys."""
+    base_batch = _dummy_batch()
     batch = {
         "observation.image.top": torch.randn(1, 3, 128, 128),
-        "observation.image.left": torch.randn(1, 3, 128, 128),
+        "observation.image.left": base_batch["observation.image.left"],
         "observation.state": [1, 2, 3, 4],
-        "action": "action_data",
+        "action": torch.tensor([[0.1, 0.2]]),
         "next.reward": 1.5,
         "next.done": True,
         "next.truncated": False,
@@ -74,7 +77,7 @@ def test_batch_to_transition_observation_grouping():
     assert transition[TransitionKey.OBSERVATION]["observation.state"] == [1, 2, 3, 4]
 
     # Check other fields
-    assert transition[TransitionKey.ACTION] == "action_data"
+    assert torch.allclose(transition[TransitionKey.ACTION], torch.tensor([[0.1, 0.2]]))
     assert transition[TransitionKey.REWARD] == 1.5
     assert transition[TransitionKey.DONE]
     assert not transition[TransitionKey.TRUNCATED]
@@ -84,15 +87,16 @@ def test_batch_to_transition_observation_grouping():
 
 def test_transition_to_batch_observation_flattening():
     """Test that transition_to_batch correctly flattens observation dict."""
+    base_batch = _dummy_batch()
     observation_dict = {
         "observation.image.top": torch.randn(1, 3, 128, 128),
-        "observation.image.left": torch.randn(1, 3, 128, 128),
+        "observation.image.left": base_batch["observation.image.left"],
         "observation.state": [1, 2, 3, 4],
     }
 
     transition = {
         TransitionKey.OBSERVATION: observation_dict,
-        TransitionKey.ACTION: "action_data",
+        TransitionKey.ACTION: torch.tensor([[0.3, 0.4]]),
         TransitionKey.REWARD: 1.5,
         TransitionKey.DONE: True,
         TransitionKey.TRUNCATED: False,
@@ -113,7 +117,7 @@ def test_transition_to_batch_observation_flattening():
     assert batch["observation.state"] == [1, 2, 3, 4]
 
     # Check other fields are mapped to next.* format
-    assert batch["action"] == "action_data"
+    assert torch.allclose(batch["action"], torch.tensor([[0.3, 0.4]]))
     assert batch["next.reward"] == 1.5
     assert batch["next.done"]
     assert not batch["next.truncated"]
@@ -123,7 +127,7 @@ def test_transition_to_batch_observation_flattening():
 def test_no_observation_keys():
     """Test behavior when there are no observation.* keys."""
     batch = {
-        "action": "action_data",
+        "action": torch.tensor([[0.7, 0.8]]),
         "next.reward": 2.0,
         "next.done": False,
         "next.truncated": True,
@@ -136,7 +140,7 @@ def test_no_observation_keys():
     assert transition[TransitionKey.OBSERVATION] is None
 
     # Check other fields
-    assert transition[TransitionKey.ACTION] == "action_data"
+    assert torch.allclose(transition[TransitionKey.ACTION], torch.tensor([[0.7, 0.8]]))
     assert transition[TransitionKey.REWARD] == 2.0
     assert not transition[TransitionKey.DONE]
     assert transition[TransitionKey.TRUNCATED]
@@ -144,7 +148,7 @@ def test_no_observation_keys():
 
     # Round trip should work
     reconstructed_batch = transition_to_batch(transition)
-    assert reconstructed_batch["action"] == "action_data"
+    assert torch.allclose(reconstructed_batch["action"], torch.tensor([[0.7, 0.8]]))
     assert reconstructed_batch["next.reward"] == 2.0
     assert not reconstructed_batch["next.done"]
     assert reconstructed_batch["next.truncated"]
@@ -153,13 +157,13 @@ def test_no_observation_keys():
 
 def test_minimal_batch():
     """Test with minimal batch containing only observation.* and action."""
-    batch = {"observation.state": "minimal_state", "action": "minimal_action"}
+    batch = {"observation.state": "minimal_state", "action": torch.tensor([[0.9]])}
 
     transition = batch_to_transition(batch)
 
     # Check observation
     assert transition[TransitionKey.OBSERVATION] == {"observation.state": "minimal_state"}
-    assert transition[TransitionKey.ACTION] == "minimal_action"
+    assert torch.allclose(transition[TransitionKey.ACTION], torch.tensor([[0.9]]))
 
     # Check defaults
     assert transition[TransitionKey.REWARD] == 0.0
@@ -171,7 +175,7 @@ def test_minimal_batch():
     # Round trip
     reconstructed_batch = transition_to_batch(transition)
     assert reconstructed_batch["observation.state"] == "minimal_state"
-    assert reconstructed_batch["action"] == "minimal_action"
+    assert torch.allclose(reconstructed_batch["action"], torch.tensor([[0.9]]))
     assert reconstructed_batch["next.reward"] == 0.0
     assert not reconstructed_batch["next.done"]
     assert not reconstructed_batch["next.truncated"]
@@ -204,9 +208,10 @@ def test_empty_batch():
 
 def test_complex_nested_observation():
     """Test with complex nested observation data."""
+    base_batch = _dummy_batch()
     batch = {
         "observation.image.top": {"image": torch.randn(1, 3, 128, 128), "timestamp": 1234567890},
-        "observation.image.left": {"image": torch.randn(1, 3, 128, 128), "timestamp": 1234567891},
+        "observation.image.left": {"image": base_batch["observation.image.left"], "timestamp": 1234567891},
         "observation.state": torch.randn(7),
         "action": torch.randn(8),
         "next.reward": 3.14,