diff --git a/baselines/logoplanner/Pi3 b/baselines/logoplanner/Pi3
index 0a505af..b412c3b 160000
--- a/baselines/logoplanner/Pi3
+++ b/baselines/logoplanner/Pi3
@@ -1 +1 @@
-Subproject commit 0a505af9c261a7c8e44946fba5b1942cdc25914b
+Subproject commit b412c3bd236dfd7686f1e4b48004d5087f2fa093
diff --git a/baselines/logoplanner/logoplanner_navbench_adapter.py b/baselines/logoplanner/logoplanner_navbench_adapter.py
new file mode 100644
index 0000000..c80f4d5
--- /dev/null
+++ b/baselines/logoplanner/logoplanner_navbench_adapter.py
@@ -0,0 +1,331 @@
+"""LoGoPlanner adapter for NavArena-Bench PointNav evaluation.
+
+This adapter bridges LoGoPlanner's HTTP REST server (logoplanner_server.py)
+with NavArena-Bench's WebSocket NavigationModelServer interface, enabling
+LoGoPlanner to be evaluated on the NavArena-Bench PointNav benchmark.
+
+Architecture:
+    NavArena-Bench runner ──WebSocket──► this adapter ──HTTP──► logoplanner_server.py
+                                                                       │
+                                                               LoGoPlanner_Agent
+                                                             (GPU inference, CUDA)
+
+Usage:
+    # Step 1 – start the LoGoPlanner HTTP server (GPU machine):
+    #   cd NavDP/baselines/logoplanner
+    #   python logoplanner_server.py --port 43020 --checkpoint ./logoplanner_policy.ckpt
+    #
+    # Step 2 – run this adapter (same or different machine):
+    #   python logoplanner_navbench_adapter.py \\
+    #       --server-url http://127.0.0.1:43020 \\
+    #       --port 8000
+
+Coordinate conventions:
+    - NavArena-Bench uses ENU world frame: yaw=0 → +X (East), CCW positive.
+    - Robot body frame: +X = forward, +Y = left.
+    - LoGoPlanner goal: (goal_x=forward, goal_y=lateral) in robot body frame.
+    - World → robot-body rotation (computed once at episode start):
+        fwd  =  cos(yaw0)*dx + sin(yaw0)*dy
+        lat  = -sin(yaw0)*dx + cos(yaw0)*dy
+
+LoGoPlanner is designed to do implicit localization from its visual-geometry
+backbone + history queues, so the goal is expressed in the episode's *starting*
+body frame and kept fixed thereafter — the model infers how far it has moved
+on its own. We therefore compute the body-frame goal once on the first
+prediction of each episode and reuse it, matching lekiwi_logoplanner_host.py.
+"""
+
+from __future__ import annotations
+
+import argparse
+import io
+import json
+import math
+
+import numpy as np
+import requests
+from PIL import Image
+
+from navarena_server.server import NavigationModelServer, serve
+
+
+class LoGoPlannerNavBenchAdapter(NavigationModelServer):
+    """NavArena-Bench PointNav agent backed by a running logoplanner_server.py."""
+
+    def __init__(
+        self,
+        server_url: str,
+        camera_intrinsic: list[list[float]],
+        stop_threshold: float = 0.3,
+        rgb_camera_key: str | None = None,
+        depth_camera_key: str | None = None,
+        request_timeout: float = 30.0,
+    ) -> None:
+        """
+        Args:
+            server_url:        Base URL of logoplanner_server.py, e.g. "http://127.0.0.1:43020".
+            camera_intrinsic:  3×3 camera intrinsic matrix as nested list [[fx,0,cx],[0,fy,cy],[0,0,1]].
+                               Used by LoGoPlanner's trajectory visualisation; adjust to match your scenes.
+            stop_threshold:    Value passed to /navigator_reset; LoGoPlanner issues a rotation-only
+                               command when max trajectory value is below this threshold.
+            rgb_camera_key:    Key into observation["rgb"] to pick the camera image (default: first key).
+            depth_camera_key:  Key into observation["depth"] (default: first key).
+            request_timeout:   HTTP request timeout in seconds.
+        """
+        self.server_url = server_url.rstrip("/")
+        self.camera_intrinsic = camera_intrinsic
+        self.stop_threshold = stop_threshold
+        self.rgb_camera_key = rgb_camera_key
+        self.depth_camera_key = depth_camera_key
+        self.request_timeout = request_timeout
+        self._goal_position: list[float] | None = None
+        self._goal_robot_frame: tuple[float, float] | None = None
+
+    # ------------------------------------------------------------------ #
+    #  Lifecycle hooks                                                     #
+    # ------------------------------------------------------------------ #
+
+    async def on_episode_start(self, payload: dict, ctx) -> None:
+        """Reset LoGoPlanner's memory queues and cache the goal for this episode."""
+        task = payload.get("task", payload)
+        goals = task.get("goals") or []
+        if goals and isinstance(goals[0], dict) and goals[0].get("position") is not None:
+            self._goal_position = list(goals[0]["position"])
+        else:
+            self._goal_position = None
+        self._goal_robot_frame = None
+        print(f"[adapter] episode_start: goal={self._goal_position}", flush=True)
+
+        resp = requests.post(
+            f"{self.server_url}/navigator_reset",
+            json={
+                "intrinsic": self.camera_intrinsic,
+                "stop_threshold": self.stop_threshold,
+                "batch_size": 1,
+            },
+            timeout=self.request_timeout,
+        )
+        resp.raise_for_status()
+
+    async def on_episode_end(self, result: dict, ctx) -> None:
+        """Finalize the per-episode mp4 so the last episode isn't left unclosed."""
+        try:
+            requests.post(
+                f"{self.server_url}/finalize",
+                timeout=self.request_timeout,
+            )
+        except Exception as exc:
+            print(f"[adapter] finalize failed: {exc}", flush=True)
+
+    # ------------------------------------------------------------------ #
+    #  Prediction                                                          #
+    # ------------------------------------------------------------------ #
+
+    async def predict(self, observation: dict, ctx) -> dict:
+        """Convert a NavBench observation to a LoGoPlanner HTTP call and return an action.
+
+        Returns:
+            dict with keys {"x", "y", "yaw"}:
+                x   – forward displacement in robot frame (metres)
+                y   – lateral displacement in robot frame (metres, positive = left)
+                yaw – rotation increment (radians); always 0 for omnidirectional mode
+        """
+        rgb_buf, depth_buf = self._encode_images(observation)
+
+        if self._goal_robot_frame is None:
+            self._goal_robot_frame = self._compute_goal_robot_frame(observation)
+            print(
+                f"[adapter] initial body-frame goal "
+                f"=({self._goal_robot_frame[0]:+.3f},{self._goal_robot_frame[1]:+.3f}) "
+                f"(fixed for the rest of the episode)",
+                flush=True,
+            )
+        goal_x, goal_y = self._goal_robot_frame
+
+        traj = self._call_pointgoal_step(rgb_buf, depth_buf, goal_x, goal_y)
+
+        # traj: list of shape (batch_size=1, predict_size, 3)
+        traj_arr = np.asarray(traj)
+        print(
+            f"[adapter] goal_robot=({goal_x:+.3f},{goal_y:+.3f}) "
+            f"traj.shape={traj_arr.shape} "
+            f"wp[0]={traj_arr[0, 0].round(3).tolist()} "
+            f"wp[4]={traj_arr[0, 4].round(3).tolist()} "
+            f"wp[-1]={traj_arr[0, -1].round(3).tolist()} "
+            f"fwd_range=[{traj_arr[0, :, 0].min():+.3f},{traj_arr[0, :, 0].max():+.3f}] "
+            f"lat_range=[{traj_arr[0, :, 1].min():+.3f},{traj_arr[0, :, 1].max():+.3f}]",
+            flush=True,
+        )
+
+        # Take the 5th waypoint of the first (only) environment.
+        first_wp = traj[0][4]
+        fwd = float(first_wp[0])
+        lat = float(first_wp[1])
+        yaw = float(first_wp[2])
+
+        return {"waypoints": [{"x": fwd, "y": lat, "yaw": yaw}]}
+
+    # ------------------------------------------------------------------ #
+    #  Internal helpers                                                    #
+    # ------------------------------------------------------------------ #
+
+    def _encode_images(self, observation: dict) -> tuple[io.BytesIO, io.BytesIO]:
+        """Encode RGB and depth observations into PNG byte buffers for multipart upload."""
+        # ── RGB: support both PIL.Image and numpy.ndarray ───────────────────
+        rgb_dict: dict = observation.get("rgb", {})
+        rgb_key = self.rgb_camera_key or next(iter(rgb_dict))
+        rgb_raw = rgb_dict[rgb_key]
+        if isinstance(rgb_raw, Image.Image):
+            rgb_np = np.asarray(rgb_raw.convert("RGB"))
+        else:
+            rgb_np = np.asarray(rgb_raw)
+            if rgb_np.ndim == 2:
+                rgb_np = np.stack([rgb_np, rgb_np, rgb_np], axis=-1)
+            if rgb_np.shape[-1] == 4:
+                rgb_np = rgb_np[:, :, :3]
+            if rgb_np.dtype != np.uint8:
+                rgb_np = np.clip(rgb_np, 0, 255).astype(np.uint8)
+
+        # NavBench observations are typically RGB; LoGoPlanner expects BGR.
+        rgb_bgr = rgb_np[:, :, ::-1].copy()   # H×W×3 uint8, BGR
+        rgb_buf = io.BytesIO()
+        Image.fromarray(rgb_bgr, mode="RGB").save(rgb_buf, format="PNG")
+        rgb_buf.seek(0)
+
+        # ── Depth: support both PIL.Image and numpy.ndarray ─────────────────
+        depth_dict: dict = observation.get("depth", {})
+        depth_key = self.depth_camera_key or next(iter(depth_dict))
+        depth_raw = depth_dict[depth_key]
+        depth_arr: np.ndarray = np.asarray(depth_raw, dtype=np.float32)
+        if depth_arr.ndim == 3:
+            depth_arr = depth_arr[:, :, 0]                   # (H, W)
+        depth_int = (depth_arr * 10000).astype(np.int32)     # metres → 0.1 mm integer
+        depth_buf = io.BytesIO()
+        Image.fromarray(depth_int, mode="I").save(depth_buf, format="PNG")
+        depth_buf.seek(0)
+
+        return rgb_buf, depth_buf
+
+    def _compute_goal_robot_frame(self, observation: dict) -> tuple[float, float]:
+        """Transform the world-frame goal into the episode's *starting* body frame.
+
+        Called exactly once per episode, on the first prediction. LoGoPlanner
+        handles the shift from start-frame to current-frame internally via its
+        implicit localization backbone.
+
+        NavBench ENU convention (matches ROS standard):
+            yaw = 0  →  robot faces +X (East)
+            yaw increases counter-clockwise
+
+        Robot body frame:
+            +X = forward,  +Y = left
+
+        Rotation (world → start-body):
+            fwd =  cos(yaw0)*dx + sin(yaw0)*dy
+            lat = -sin(yaw0)*dx + cos(yaw0)*dy
+        """
+        pose = observation.get("pose", {})
+        robot_pos = pose.get("position", [0.0, 0.0, 0.0])
+        robot_yaw: float = pose.get("yaw", 0.0)
+
+        if self._goal_position is not None:
+            goal_pos = self._goal_position
+        else:
+            goal = observation.get("goal", {})
+            goal_pos = goal.get("position", [0.0, 0.0, 0.0])
+
+        dx = goal_pos[0] - robot_pos[0]
+        dy = goal_pos[1] - robot_pos[1]
+
+        cos_y = math.cos(robot_yaw)
+        sin_y = math.sin(robot_yaw)
+        goal_fwd = cos_y * dx + sin_y * dy
+        goal_lat = -sin_y * dx + cos_y * dy
+
+        return goal_fwd, goal_lat
+
+    def _call_pointgoal_step(
+        self,
+        rgb_buf: io.BytesIO,
+        depth_buf: io.BytesIO,
+        goal_x: float,
+        goal_y: float,
+    ) -> list:
+        """POST to /pointgoal_step and return the parsed trajectory list."""
+        files = {
+            "image": ("image.png", rgb_buf, "image/png"),
+            "depth": ("depth.png", depth_buf, "image/png"),
+        }
+        data = {
+            "goal_data": json.dumps({
+                "goal_x": [goal_x],   # list of length batch_size=1
+                "goal_y": [goal_y],
+            })
+        }
+        resp = requests.post(
+            f"{self.server_url}/pointgoal_step",
+            files=files,
+            data=data,
+            timeout=self.request_timeout,
+        )
+        resp.raise_for_status()
+        return resp.json()["trajectory"]
+
+
+# ──────────────────────────────────────────────────────────────────────── #
+#  CLI entry point                                                          #
+# ──────────────────────────────────────────────────────────────────────── #
+
+def _build_intrinsic(fx: float, fy: float, cx: float, cy: float) -> list[list[float]]:
+    return [[fx, 0.0, cx], [0.0, fy, cy], [0.0, 0.0, 1.0]]
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="LoGoPlanner → NavArena-Bench PointNav adapter",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "--server-url", default="http://127.0.0.1:43020",
+        help="URL of the running logoplanner_server.py",
+    )
+    parser.add_argument("--host", default="0.0.0.0", help="WebSocket bind host")
+    parser.add_argument("--port", type=int, default=8000, help="WebSocket bind port")
+    parser.add_argument(
+        "--stop-threshold", type=float, default=-10.0,
+        help="LoGoPlanner stop threshold forwarded to /navigator_reset",
+    )
+    # Camera intrinsic (for LoGoPlanner trajectory visualisation).
+    # Default matches the NavArena-Bench gs_env default camera (fx=fy=320, cx=320, cy=240).
+    parser.add_argument("--fx", type=float, default=320.0, help="Camera intrinsic fx")
+    parser.add_argument("--fy", type=float, default=320.0, help="Camera intrinsic fy")
+    parser.add_argument("--cx", type=float, default=320.0, help="Camera intrinsic cx (principal point x)")
+    parser.add_argument("--cy", type=float, default=240.0, help="Camera intrinsic cy (principal point y)")
+    parser.add_argument(
+        "--rgb-key", default=None,
+        help="Camera key in observation['rgb'] (default: first available key)",
+    )
+    parser.add_argument(
+        "--depth-key", default=None,
+        help="Camera key in observation['depth'] (default: first available key)",
+    )
+    parser.add_argument("--timeout", type=float, default=30.0, help="HTTP request timeout (seconds)")
+    args = parser.parse_args()
+
+    intrinsic = _build_intrinsic(args.fx, args.fy, args.cx, args.cy)
+    adapter = LoGoPlannerNavBenchAdapter(
+        server_url=args.server_url,
+        camera_intrinsic=intrinsic,
+        stop_threshold=args.stop_threshold,
+        rgb_camera_key=args.rgb_key,
+        depth_camera_key=args.depth_key,
+        request_timeout=args.timeout,
+    )
+
+    print(f"LoGoPlanner server : {args.server_url}")
+    print(f"NavBench WebSocket : ws://{args.host}:{args.port}")
+    serve(adapter, host=args.host, port=args.port)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/baselines/logoplanner/logoplanner_server.py b/baselines/logoplanner/logoplanner_server.py
index 401c41c..071a1ab 100644
--- a/baselines/logoplanner/logoplanner_server.py
+++ b/baselines/logoplanner/logoplanner_server.py
@@ -12,7 +12,7 @@
 from PIL import Image, ImageDraw, ImageFont
 import argparse
 parser = argparse.ArgumentParser()
-parser.add_argument("--port",type=int,default=8888)
+parser.add_argument("--port",type=int,default=43020)
 parser.add_argument("--checkpoint",type=str,default="./logoplanner_policy.ckpt")
 args = parser.parse_known_args()[0]
 
@@ -57,6 +57,14 @@ def logoplanner_reset_env():
     logoplanner_navigator.reset_env(int(request.get_json().get('env_id')))
     return jsonify({"algo":"logoplanner"})
 
+@app.route("/finalize",methods=['POST'])
+def logoplanner_finalize():
+    global logoplanner_fps_writer
+    if logoplanner_fps_writer is not None:
+        logoplanner_fps_writer.close()
+        logoplanner_fps_writer = None
+    return jsonify({"algo":"logoplanner","status":"closed"})
+
 @app.route("/pointgoal_step",methods=['POST'])
 def logoplanner_step_xy():
     global logoplanner_navigator,logoplanner_fps_writer
diff --git a/baselines/logoplanner/policy_network.py b/baselines/logoplanner/policy_network.py
index 46e8cc3..abafe29 100644
--- a/baselines/logoplanner/policy_network.py
+++ b/baselines/logoplanner/policy_network.py
@@ -126,9 +126,7 @@ def predict_pointgoal_action(self,start_goal,memory_rgbd,context_rgbd,sample_num
             
             all_trajectory = torch.cumsum(naction / 4.0, dim=1)
             all_trajectory = all_trajectory.reshape(start_goal.shape[0],sample_num,self.predict_size,3)
-            trajectory_length = all_trajectory[:,:,-1,0:2].norm(dim=-1)
-            all_trajectory[trajectory_length < 0.5] = all_trajectory[trajectory_length < 0.5] * torch.tensor([[[0,0,1.0]]],device=all_trajectory.device)
-            
+
             sorted_indices = (-critic_values).argsort(dim=1)
             topk_indices = sorted_indices[:,0:2]
             batch_indices = torch.arange(start_goal.shape[0]).unsqueeze(1).expand(-1, 2)