feat(vla): add SmolVLA conditioned agent with prefix encoder and train/eval validation

Introduce SmolVLA-style VLM prefix encoder backbone and IMF-AttnRes
conditioned agent. Add episode-level train/val split, action MSE
validation in training, and headless eval support.

tests/test_eval_vla_headless.py

@@ -15,6 +15,7 @@ class _FakeAgent:
     def __init__(self):
         self.reset_calls = 0
         self.last_observation = None
+        self.observation_shapes = []
 
     def eval(self):
         return self
@@ -27,6 +28,7 @@ class _FakeAgent:
 
     def select_action(self, observation):
         self.last_observation = observation
+        self.observation_shapes.append(tuple(observation["images"]["front"].shape))
         return torch.zeros(16)
 
 
@@ -108,6 +110,23 @@ class EvalVLAHeadlessTest(unittest.TestCase):
         self.assertEqual(tuple(prepared["images"]["front"].shape), (3, 8, 8))
         self.assertEqual(tuple(prepared["qpos"].shape), (16,))
 
+    def test_prepare_observation_preserves_task_when_present(self):
+        obs = {
+            "images": {
+                "front": np.zeros((4, 4, 3), dtype=np.uint8),
+            },
+            "qpos": np.zeros(16, dtype=np.float32),
+            "task": "insert the peg into the socket",
+        }
+
+        prepared = eval_vla.prepare_observation(
+            obs,
+            ["front"],
+            image_resize_shape=None,
+        )
+
+        self.assertEqual(prepared["task"], "insert the peg into the socket")
+
     def test_headless_eval_sets_mujoco_gl_to_egl_when_display_missing(self):
         cfg = OmegaConf.create({"eval": {"headless": True}})
         with mock.patch.dict(eval_vla.os.environ, {}, clear=True):
@@ -125,6 +144,8 @@ class EvalVLAHeadlessTest(unittest.TestCase):
 
         self.assertIn("headless", eval_cfg)
         self.assertFalse(eval_cfg.headless)
+        self.assertIn("task_description", eval_cfg)
+        self.assertIsNone(eval_cfg.task_description)
 
     def test_make_sim_env_accepts_headless_and_disables_render(self):
         fake_env = object()
@@ -291,6 +312,74 @@ class EvalVLAHeadlessTest(unittest.TestCase):
         self.assertIsNotNone(fake_agent.last_observation)
         self.assertIn("front", fake_agent.last_observation["images"])
 
+    def test_eval_main_uses_condition_encoder_resize_override(self):
+        fake_env = _FakeEnv()
+        fake_agent = _FakeAgent()
+        cfg = OmegaConf.create(
+            {
+                "agent": {
+                    "condition_encoder": {
+                        "eval_image_resize_shape": None,
+                    },
+                },
+                "data": {
+                    "image_resize_shape": [224, 224],
+                },
+                "eval": {
+                    "ckpt_path": "checkpoints/vla_model_best.pt",
+                    "num_episodes": 1,
+                    "max_timesteps": 1,
+                    "device": "cpu",
+                    "task_name": "sim_transfer",
+                    "camera_names": ["front"],
+                    "use_smoothing": False,
+                    "smooth_alpha": 0.3,
+                    "verbose_action": False,
+                    "headless": True,
+                },
+            }
+        )
+
+        with mock.patch.object(eval_vla, "load_checkpoint", return_value=(fake_agent, None)), \
+             mock.patch.object(eval_vla, "make_sim_env", return_value=fake_env), \
+             mock.patch.object(eval_vla, "sample_transfer_pose", return_value=np.array([0.1, 0.2, 0.3])), \
+             mock.patch.object(eval_vla, "execute_policy_action"), \
+             mock.patch.object(eval_vla, "tqdm", side_effect=lambda iterable, **kwargs: iterable):
+            eval_vla.main.__wrapped__(cfg)
+
+        self.assertEqual(fake_agent.observation_shapes, [(3, 8, 8)])
+
+    def test_eval_main_injects_configured_task_description_when_env_omits_task(self):
+        fake_env = _FakeEnv()
+        fake_agent = _FakeAgent()
+        cfg = OmegaConf.create(
+            {
+                "agent": {},
+                "eval": {
+                    "ckpt_path": "checkpoints/vla_model_best.pt",
+                    "num_episodes": 1,
+                    "max_timesteps": 1,
+                    "device": "cpu",
+                    "task_name": "sim_transfer",
+                    "task_description": "pick the red cube",
+                    "camera_names": ["front"],
+                    "use_smoothing": False,
+                    "smooth_alpha": 0.3,
+                    "verbose_action": False,
+                    "headless": True,
+                },
+            }
+        )
+
+        with mock.patch.object(eval_vla, "load_checkpoint", return_value=(fake_agent, None)), \
+             mock.patch.object(eval_vla, "make_sim_env", return_value=fake_env), \
+             mock.patch.object(eval_vla, "sample_transfer_pose", return_value=np.array([0.1, 0.2, 0.3])), \
+             mock.patch.object(eval_vla, "execute_policy_action"), \
+             mock.patch.object(eval_vla, "tqdm", side_effect=lambda iterable, **kwargs: iterable):
+            eval_vla.main.__wrapped__(cfg)
+
+        self.assertEqual(fake_agent.last_observation["task"], "pick the red cube")
+
     def test_run_eval_returns_average_reward_summary(self):
         reward_sequences = [
             [1.0, 2.0],