diff --git a/gym_dora/gym_dora/env.py b/gym_dora/gym_dora/env.py
index 5316fc728..2772ea66d 100644
--- a/gym_dora/gym_dora/env.py
+++ b/gym_dora/gym_dora/env.py
@@ -6,11 +6,11 @@ import pyarrow as pa
 from dora import Node
 from gymnasium import spaces
 
+FPS = int(os.getenv("FPS", "30"))
 IMAGE_WIDTH = int(os.getenv("IMAGE_WIDTH", "640"))
 IMAGE_HEIGHT = int(os.getenv("IMAGE_HEIGHT", "480"))
-FPS = int(os.getenv("FPS", "30"))
 
-JOINTS = [
+ALOHA_JOINTS = [
     # absolute joint position
     "left_arm_waist",
     "left_arm_shoulder",
@@ -30,8 +30,7 @@ JOINTS = [
     # normalized gripper position 0: close, 1: open
     "right_arm_gripper",
 ]
-
-ACTIONS = [
+ALOHA_ACTIONS = [
     # position and quaternion for end effector
     "left_arm_waist",
     "left_arm_shoulder",
@@ -55,56 +54,104 @@ ACTIONS = [
 class DoraEnv(gym.Env):
     metadata = {"render_modes": ["rgb_array"], "render_fps": FPS}
 
-    def __init__(self, model="aloha"):
+    def __init__(
+        self,
+        model="aloha",
+        observation_width=IMAGE_WIDTH,
+        observation_height=IMAGE_HEIGHT,
+        cameras_names=None,
+        num_joints=None,
+        num_actions=None,
+    ):
+        """Initializes the Dora environment.
+
+        Args:
+            model (str): The model to use. Either 'aloha' or 'custom'.
+            observation_width (int): The width of the observation image.
+            observation_height (int): The height of the observation image.
+            cameras_names (list): A list of camera names to use. If not provided, the default is ['cam_high', 'cam_low', 'cam_left_wrist', 'cam_right_wrist'].
+            num_joints (int): The number of joints in the model. If not provided, the default is 14 for 'aloha' and 6 for 'fivedof'.
+            num_actions (int): The number of actions in the model. If not provided, the default is 14 for 'aloha' and 6 for 'fivedof'.
+        """
+        super().__init__()
+
         # Initialize a new node
-        self.node = Node()
+        self.node = Node() if os.environ.get("DORA_NODE_CONFIG", None) is not None else None
         self.observation = {"pixels": {}, "agent_pos": None}
         self.terminated = False
 
-        self.observation_height = IMAGE_HEIGHT
-        self.observation_width = IMAGE_WIDTH
+        self.observation_height = observation_height
+        self.observation_width = observation_width
 
-        assert model == "aloha"
-        self.observation_space = spaces.Dict(
-            {
-                "pixels": spaces.Dict(
-                    {
-                        "cam_high": spaces.Box(
-                            low=0,
-                            high=255,
-                            shape=(self.observation_height, self.observation_width, 3),
-                            dtype=np.uint8,
-                        ),
-                        "cam_low": spaces.Box(
-                            low=0,
-                            high=255,
-                            shape=(self.observation_height, self.observation_width, 3),
-                            dtype=np.uint8,
-                        ),
-                        "cam_left_wrist": spaces.Box(
-                            low=0,
-                            high=255,
-                            shape=(self.observation_height, self.observation_width, 3),
-                            dtype=np.uint8,
-                        ),
-                        "cam_right_wrist": spaces.Box(
-                            low=0,
-                            high=255,
-                            shape=(self.observation_height, self.observation_width, 3),
-                            dtype=np.uint8,
-                        ),
-                    }
-                ),
-                "agent_pos": spaces.Box(
-                    low=-1000.0,
-                    high=1000.0,
-                    shape=(len(JOINTS),),
-                    dtype=np.float64,
-                ),
-            }
-        )
+        # Observation space
+        if model == "aloha":
+            self.observation_space = spaces.Dict(
+                {
+                    "pixels": spaces.Dict(
+                        {
+                            "cam_high": spaces.Box(
+                                low=0,
+                                high=255,
+                                shape=(self.observation_height, self.observation_width, 3),
+                                dtype=np.uint8,
+                            ),
+                            "cam_low": spaces.Box(
+                                low=0,
+                                high=255,
+                                shape=(self.observation_height, self.observation_width, 3),
+                                dtype=np.uint8,
+                            ),
+                            "cam_left_wrist": spaces.Box(
+                                low=0,
+                                high=255,
+                                shape=(self.observation_height, self.observation_width, 3),
+                                dtype=np.uint8,
+                            ),
+                            "cam_right_wrist": spaces.Box(
+                                low=0,
+                                high=255,
+                                shape=(self.observation_height, self.observation_width, 3),
+                                dtype=np.uint8,
+                            ),
+                        }
+                    ),
+                    "agent_pos": spaces.Box(
+                        low=-1000.0,
+                        high=1000.0,
+                        shape=(len(ALOHA_JOINTS),),
+                        dtype=np.float64,
+                    ),
+                }
+            )
+        elif model == "custom":
+            pixel_dict = {}
+            for camera in cameras_names:
+                assert camera.startswith("cam"), "Camera names must start with 'cam'"
+                pixel_dict[camera] = spaces.Box(
+                    low=0,
+                    high=255,
+                    shape=(self.observation_height, self.observation_width, 3),
+                    dtype=np.uint8,
+                )
+            self.observation_space = spaces.Dict(
+                {
+                    "pixels": spaces.Dict(pixel_dict),
+                    "agent_pos": spaces.Box(
+                        low=-1000.0,
+                        high=1000.0,
+                        shape=(num_joints,),
+                        dtype=np.float64,
+                    ),
+                }
+            )
+        else:
+            raise ValueError("Model must be either 'aloha' or 'custom'.")
 
-        self.action_space = spaces.Box(low=-1, high=1, shape=(len(ACTIONS),), dtype=np.float32)
+        # Action space
+        if model == "aloha":
+            self.action_space = spaces.Box(low=-1, high=1, shape=(len(ALOHA_ACTIONS),), dtype=np.float32)
+        elif model == "custom":
+            self.action_space = spaces.Box(low=-1, high=1, shape=(num_actions,), dtype=np.float32)
 
     def _get_obs(self):
         while True:
@@ -119,7 +166,7 @@ class DoraEnv(gym.Env):
                 # Map Image input into pixels key within Aloha environment
                 if "cam" in event["id"]:
                     self.observation["pixels"][event["id"]] = (
-                        event["value"].to_numpy().reshape(IMAGE_HEIGHT, IMAGE_WIDTH, 3)
+                        event["value"].to_numpy().reshape(self.observation_height, self.observation_width, 3)
                     )
                 else:
                     # Map other inputs into the observation dictionary using the event id as key