diff --git a/ManiSkill2_real2sim b/ManiSkill2_real2sim
index ef7a4d4f..89f45138 160000
--- a/ManiSkill2_real2sim
+++ b/ManiSkill2_real2sim
@@ -1 +1 @@
-Subproject commit ef7a4d4fdf4b69f2c2154db5b15b9ac8dfe10682
+Subproject commit 89f451381ae7cc3c23d010d374868943a3d387c9
diff --git a/evaluation_metadata.json b/evaluation_metadata.json
new file mode 100644
index 00000000..d2d2af14
--- /dev/null
+++ b/evaluation_metadata.json
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diff --git a/rollout_test.py b/rollout_test.py
new file mode 100644
index 00000000..91d2ad1d
--- /dev/null
+++ b/rollout_test.py
@@ -0,0 +1,64 @@
+import numpy as np
+import mediapy
+import simpler_env
+import sapien.core as sapien
+
+from simpler_env.utils.env.observation_utils import (
+    get_image_from_maniskill2_obs_dict
+)
+
+from simpler_env.policies.rt1.rt1_model import RT1Inference
+
+
+task_name = "google_robot_pick_coke_can"
+
+env = simpler_env.make(task_name)
+
+sapien.render_config.rt_use_denoiser = False
+
+model = RT1Inference(
+    saved_model_path="./checkpoints/rt_1_x_tf_trained_for_002272480_step",
+    policy_setup="google_robot"
+)
+
+obs, reset_info = env.reset()
+
+instruction = env.get_language_instruction()
+
+print("Instruction:", instruction)
+
+model.reset(instruction)
+
+image = get_image_from_maniskill2_obs_dict(env, obs)
+
+frames = []
+
+predicted_terminated = False
+truncated = False
+success = False
+
+while not (predicted_terminated or truncated):
+
+    raw_action, action = model.step(image)
+
+    predicted_terminated = bool(
+        action["terminate_episode"][0] > 0
+    )
+
+    obs, reward, success, truncated, info = env.step(
+        np.concatenate([
+            action["world_vector"],
+            action["rot_axangle"],
+            action["gripper"]
+        ])
+    )
+
+    image = get_image_from_maniskill2_obs_dict(env, obs)
+
+    frames.append(image)
+
+print("Success:", success)
+
+mediapy.write_video("rollout_1.mp4", frames, fps=10)
+
+print("Video saved: rollout_1.mp4")
diff --git a/run_eval.py b/run_eval.py
new file mode 100644
index 00000000..c29913b0
--- /dev/null
+++ b/run_eval.py
@@ -0,0 +1,129 @@
+import os
+import numpy as np
+import mediapy
+import simpler_env
+import sapien.core as sapien
+
+from simpler_env.utils.env.observation_utils import (
+    get_image_from_maniskill2_obs_dict
+)
+
+from simpler_env.policies.rt1.rt1_model import RT1Inference
+
+
+
+TASK_NAME = "google_robot_pick_coke_can"
+
+# seeds для baseline и distractors
+SEEDS = list(range(100, 120))
+
+# 0 = baseline
+# 1 = один distractor
+# 3 = три distractors
+DISTRACTOR_LEVEL = 3
+
+OUTPUT_DIR = f"results/distractors_level_{DISTRACTOR_LEVEL}"
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+
+sapien.render_config.rt_use_denoiser = False
+
+env = simpler_env.make(TASK_NAME)
+env.unwrapped.distractor_level = DISTRACTOR_LEVEL
+
+model = RT1Inference(
+    saved_model_path="./checkpoints/rt_1_x_tf_trained_for_002272480_step",
+    policy_setup="google_robot"
+)
+success_count = 0
+
+# MAIN
+
+for episode_idx, seed in enumerate(SEEDS):
+
+    print("\n" + "=" * 60)
+    print(f"EPISODE {episode_idx}")
+    print(f"SEED: {seed}")
+    print("=" * 60)
+
+    np.random.seed(seed)
+
+    reset_options = {}
+
+    if DISTRACTOR_LEVEL == 1:
+        reset_options["distractor_model_ids"] = [
+            "tetra_pak_carton"
+        ]
+
+    elif DISTRACTOR_LEVEL == 3:
+        reset_options["distractor_model_ids"] = [
+            "chips_bag",
+            "tetra_pak_carton",
+            "tin_can"
+        ]
+
+    obs, reset_info = env.reset(
+        seed=seed,
+        options=reset_options
+    )
+
+    instruction = env.get_language_instruction()
+    print("Instruction:", instruction)
+
+    model.reset(instruction)
+
+    image = get_image_from_maniskill2_obs_dict(env, obs)
+
+    frames = []
+
+    predicted_terminated = False
+    truncated = False
+    success = False
+
+    step_count = 0
+
+    while not (predicted_terminated or truncated):
+
+        raw_action, action = model.step(image)
+
+        predicted_terminated = bool(
+            action["terminate_episode"][0] > 0
+        )
+
+        obs, reward, success, truncated, info = env.step(
+            np.concatenate([
+                action["world_vector"],
+                action["rot_axangle"],
+                action["gripper"]
+            ])
+        )
+
+        image = get_image_from_maniskill2_obs_dict(env, obs)
+
+        frames.append(image)
+
+        step_count += 1
+
+    # сохраняем только первые 5 rollout
+    video_path = os.path.join(
+        OUTPUT_DIR,
+        f"rollout_seed_{seed}.mp4"
+    )
+
+    mediapy.write_video(video_path, frames, fps=10)
+
+    print("Video saved:", video_path)
+
+    print("Success:", success)
+    if success:
+       success_count += 1
+    print("Steps:", step_count)
+
+
+sr = success_count / len(SEEDS)
+
+print("\n\n")
+print("FINAL RESULTS")
+print("=" * 60)
+print(f"Success Rate: {sr:.2f}")
diff --git a/test_env.py b/test_env.py
new file mode 100644
index 00000000..85909a4a
--- /dev/null
+++ b/test_env.py
@@ -0,0 +1,40 @@
+import simpler_env
+import numpy as np
+import mediapy
+
+from simpler_env.utils.env.observation_utils import (
+    get_image_from_maniskill2_obs_dict
+)
+
+env = simpler_env.make(
+    "google_robot_pick_coke_can"
+)
+
+obs, info = env.reset(
+    options={
+        "distractor_model_ids": [
+            "chips_bag",
+            "tetra_pak_carton",
+            "tin_can"
+        ]
+    }
+)
+
+print("Distractors:", env.unwrapped.distractor_objs)
+print("Count:", len(env.unwrapped.distractor_objs))
+
+frames = []
+
+for i in range(10):
+
+    action = np.zeros(7)
+
+    obs, reward, success, truncated, info = env.step(action)
+
+    image = get_image_from_maniskill2_obs_dict(env, obs)
+
+    frames.append(image)
+
+mediapy.write_video("debug_scene.mp4", frames, fps=5)
+
+print("saved")
diff --git a/tutorial_run.pdf b/tutorial_run.pdf
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