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@@ -791,3 +791,367 @@ GR00T-WholeBodyControl/decoupled_wbc/dexmg/gr00trobocasa/robocasa/models/assets/
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Isaac-GR00T/examples/DROID/README.md

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+# GR00T DROID
+
+The N1.7 base model supports DROID inference out of the box via the `OXE_DROID_RELATIVE_EEF_RELATIVE_JOINT` pretrain tag. A finetuned checkpoint is also available at [`nvidia/GR00T-N1.7-DROID`](https://huggingface.co/nvidia/GR00T-N1.7-DROID).
+
+> **Note:** The DROID dataset contains multiple language instruction paraphrases per episode (`language_instruction`, `language_instruction_2`, `language_instruction_3`). These are used for language augmentation during training. At inference time, only the first language key is used.
+
+## Data Format
+
+The DROID embodiment expects the following modality structure:
+
+| Modality | Keys | Dimensions |
+|----------|------|------------|
+| Video | `exterior_image_1_left`, `wrist_image_left` | 2 cameras |
+| State | `eef_9d`, `gripper_position`, `joint_position` | 9D + 1D + 7D = 17D |
+| Action | `eef_9d`, `gripper_position`, `joint_position` | 9D + 1D + 7D = 17D |
+| Language | `annotation.language.language_instruction` | text |
+
+Action representations:
+- `eef_9d`: relative end-effector (XYZ + rotation 6D)
+- `gripper_position`: absolute (1D)
+- `joint_position`: relative joint positions (7D)
+
+### Preparing DROID Demo Data
+
+The full DROID dataset ([lerobot/droid_1.0.1](https://huggingface.co/datasets/lerobot/droid_1.0.1)) is ~358 GB with 95k+ episodes in LeRobot v3.0 format. To create a small sample for testing:
+
+```bash
+uv pip install jsonlines   # one-time dependency
+python scripts/download_droid_sample.py
+```
+
+This downloads the first data/video chunk (~170 MB) and extracts 3 episodes into `demo_data/droid_sample/` in GR00T LeRobot v2.0 format.
+
+**Key conversion notes:**
+- Source is LeRobot v3.0 (consolidated parquet + concatenated videos) — the script converts to v2.0 (per-episode parquet + per-episode mp4).
+- Video keys in the raw dataset (`exterior_1_left`, `wrist_left`) differ from the model config keys (`exterior_image_1_left`, `wrist_image_left`). The data loader auto-maps by position — no manual renaming needed.
+- Language instructions are loaded via the `task_index` column mapped through `tasks.jsonl`.
+
+## 1. Standalone Inference (with demo data)
+
+After preparing demo data, run inference directly (no server needed):
+
+```bash
+uv run python scripts/deployment/standalone_inference_script.py \
+    --model-path nvidia/GR00T-N1.7-3B \
+    --dataset-path demo_data/droid_sample \
+    --embodiment-tag OXE_DROID_RELATIVE_EEF_RELATIVE_JOINT \
+    --traj-ids 0 1 \
+    --inference-mode pytorch \
+    --execution-horizon 8
+```
+
+> **Note:** Episode 0 may have an empty language instruction. If inference fails on episode 0, try `--traj-ids 1 2`.
+
+Expected zero-shot performance on the base model (not finetuned):
+
+| Metric | Value |
+|--------|-------|
+| Average MSE | ~0.0149 |
+| Average MAE | ~0.0753 |
+| Inference per step (base) | ~262 ms (H100) |
+| Inference per step (finetuned) | ~253 ms (H100) |
+
+## 2. Inference Server (for real-world deployment)
+
+### Using the base model (zero-shot):
+
+```bash
+uv run python gr00t/eval/run_gr00t_server.py \
+    --model-path nvidia/GR00T-N1.7-3B \
+    --embodiment-tag OXE_DROID_RELATIVE_EEF_RELATIVE_JOINT
+```
+
+### Using the finetuned model:
+
+```bash
+uv run python gr00t/eval/run_gr00t_server.py \
+    --model-path nvidia/GR00T-N1.7-DROID \
+    --embodiment-tag OXE_DROID_RELATIVE_EEF_RELATIVE_JOINT
+```
+
+## 3. Fine-tuning
+
+Fine-tune the base model on DROID data using the shared launcher:
+
+```bash
+NUM_GPUS=8 MAX_STEPS=20000 GLOBAL_BATCH_SIZE=640 SAVE_STEPS=1000 uv run bash examples/finetune.sh \
+    --base-model-path nvidia/GR00T-N1.7-3B \
+    --dataset-path demo_data/droid_sample \
+    --embodiment-tag OXE_DROID_RELATIVE_EEF_RELATIVE_JOINT \
+    --output-dir /tmp/droid_finetune
+```
+
+> **Note:** The above uses the small `demo_data/droid_sample` (3 episodes) for quick validation. For production training, replace `--dataset-path` with the full DROID dataset.
+
+## 4. Robot Control Script
+
+1. Install the DROID package on the robot control laptop/workstation — [instructions](https://droid-dataset.github.io/droid/software-setup/host-installation.html#configuring-the-laptopworkstation)
+
+2. Install dependencies for the GR00T control script in the environment from step 1:
+```bash
+pip install tyro pydantic numpy==1.26.4
+```
+
+3. Enter the camera IDs for your ZED cameras in `examples/DROID/main_gr00t.py`.
+
+4. Start the control script:
+```bash
+python examples/DROID/main_gr00t.py --external-camera="left" # or "right"
+```

Isaac-GR00T/examples/DROID/main_gr00t.py

@@ -0,0 +1,503 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# ruff: noqa
+# NOTE: this requires installation of the droid repo.
+# Adapted from https://github.com/Physical-Intelligence/openpi/blob/main/examples/droid/main.py
+
+from __future__ import annotations
+
+import contextlib
+import dataclasses
+import datetime
+import faulthandler
+import os
+import signal
+import time
+from collections import deque
+
+import cv2
+import numpy as np
+import pandas as pd
+import tqdm
+import tyro
+from PIL import Image
+
+from scipy.spatial.transform import Rotation
+
+faulthandler.enable()
+
+# DROID data collection frequency -- we slow down execution to match this frequency
+DROID_CONTROL_FREQUENCY = 15
+RESOLUTION = (180, 320)  # resize images to this resolution before sending to the policy server
+
+# Egocentric frame correction: R_euler is post-multiplied by this matrix
+# to match the OXE DROID training pipeline (TFG convention).
+# audit: mirror of gr00t/data/state_action/droid_frame.py — this example runs on
+# a slim DROID install without the gr00t package, so it cannot import the
+# canonical source. Keep this matrix and compute_eef_9d in sync with it.
+DROID_EEF_ROTATION_CORRECT = np.array(
+    [[0, 0, -1], [-1, 0, 0], [0, 1, 0]],
+    dtype=np.float64,
+)
+
+
+def compute_eef_9d(cartesian_position: np.ndarray) -> np.ndarray:
+    """Convert cartesian_position (XYZ + euler 3D) to eef_9d (XYZ + rot6d).
+
+    Uses extrinsic XYZ Euler convention (scipy ``"XYZ"``, equivalent to
+    ``tfg.rotation_matrix_3d.from_euler``) and post-multiplies by
+    ``DROID_EEF_ROTATION_CORRECT`` to match the pretrained model.
+    """
+    c = np.asarray(cartesian_position, dtype=np.float64).reshape(6)
+    xyz = c[:3]
+    euler = c[3:6]
+    rot_robot = Rotation.from_euler("XYZ", euler).as_matrix()
+    rot_mat = rot_robot @ DROID_EEF_ROTATION_CORRECT
+    rot6d = rot_mat[:2, :].reshape(6)
+    return np.concatenate([xyz, rot6d]).astype(np.float32)
+
+
+@dataclasses.dataclass
+class Args:
+    # Hardware parameters
+
+    left_camera_id: str = "<SET THIS>"  # e.g., "24259877"
+    right_camera_id: str = "<SET THIS>"  # e.g., "24514023"
+    wrist_camera_id: str = "<SET THIS>"  # e.g., "13062452"
+
+    # Policy parameters
+    policy_host: str = "localhost"
+    policy_port: int = 5555
+    policy_api_token: str = None
+
+    results_dir: str = None  # if None, will use the current timestamp as the results directory
+
+    # Rollout parameters
+    max_timesteps: int = 600  # how many steps to run each rollout
+
+    # How many actions to execute from a predicted action chunk before querying policy server again
+    open_loop_horizon: int = 15
+    external_camera: str = (
+        "left"  # which exterior camera to use for the policy server, choose from ["left", "right"]
+    )
+    render_camera: str = "left"  # which camera to render saved video from
+    render_fps: int = 50
+
+    debug: bool = False
+    vis_cameras: bool = False
+
+    delay_seconds: int = 5
+
+
+def _load_runtime_deps():
+    """Load robot-only dependencies after CLI parsing so --help stays usable."""
+    try:
+        import imageio
+        from droid.robot_env import RobotEnv
+        from server_client import PolicyClient
+        from utils import resize_with_pad
+    except ModuleNotFoundError as exc:
+        raise ModuleNotFoundError(
+            "examples/DROID/main_gr00t.py requires the DROID robot-control environment. "
+            "Follow examples/DROID/README.md and install the DROID package plus "
+            "`pip install tyro pydantic numpy==1.26.4` before running."
+        ) from exc
+
+    return RobotEnv, PolicyClient, imageio, resize_with_pad
+
+
+# We are using Ctrl+C to optionally terminate rollouts early -- however, if we press Ctrl+C while the policy server is
+# waiting for a new action chunk, it will raise an exception and the server connection dies.
+# This context manager temporarily prevents Ctrl+C and delays it after the server call is complete.
+@contextlib.contextmanager
+def prevent_keyboard_interrupt():
+    """Temporarily prevent keyboard interrupts by delaying them until after the protected code."""
+    interrupted = False
+    original_handler = signal.getsignal(signal.SIGINT)
+
+    def handler(signum, frame):
+        nonlocal interrupted
+        interrupted = True
+
+    signal.signal(signal.SIGINT, handler)
+    try:
+        yield
+    finally:
+        signal.signal(signal.SIGINT, original_handler)
+        if interrupted:
+            raise KeyboardInterrupt
+
+
+def main(args: Args):
+    RobotEnv, PolicyClient, imageio, resize_with_pad = _load_runtime_deps()
+
+    assert args.external_camera in ["left", "right"], (
+        f"Invalid exterior camera: {args.exterior_camera}"
+    )
+
+    if args.results_dir is None:
+        results_dir = f"results_gr00t_{datetime.datetime.now().strftime('%Y_%m_%d')}"
+    else:
+        results_dir = args.results_dir
+
+    # Initialize the Panda environment.
+    env = RobotEnv(action_space="joint_position", gripper_action_space="position")
+    print("Created the droid env!")
+
+    os.makedirs(results_dir, exist_ok=True)
+
+    policy_client = PolicyClient(
+        host=args.policy_host, port=args.policy_port, api_token=args.policy_api_token
+    )
+
+    modality_config = policy_client.get_modality_config()
+    video_delta = modality_config["video"].delta_indices
+    video_T = len(video_delta)
+    video_history_len = max(-min(video_delta), 0) + 1 if video_delta else 1
+    video_keys = modality_config["video"].modality_keys
+    state_keys = modality_config["state"].modality_keys
+    state_T = len(modality_config["state"].delta_indices)
+    # Action chunk size is dictated by the policy (server) side. open_loop_horizon
+    # is the only locally-authored horizon; it is a deliberate receding-horizon
+    # choice and MAY be < the chunk, but it must never exceed it, otherwise
+    # `pred_action_chunk[actions_from_chunk_completed]` indexes past the
+    # predicted chunk and IndexErrors mid-rollout. Source the chunk size from
+    # the policy and validate the contract up-front instead of crashing deep in
+    # the loop.
+    action_chunk_size = len(modality_config["action"].delta_indices)
+    if not (1 <= args.open_loop_horizon <= action_chunk_size):
+        raise ValueError(
+            f"open_loop_horizon={args.open_loop_horizon} must satisfy "
+            f"1 <= open_loop_horizon <= action_chunk_size={action_chunk_size} "
+            "(= len(policy.action.delta_indices)). A larger value would index "
+            "past the predicted action chunk and IndexError mid-rollout."
+        )
+    print(
+        f"Model config — video T={video_T} (delta={video_delta}), "
+        f"state T={state_T}, action chunk={action_chunk_size}, "
+        f"open_loop_horizon={args.open_loop_horizon}, "
+        f"keys: video={video_keys}, state={state_keys}"
+    )
+
+    df = pd.DataFrame(columns=["success", "duration", "video_filename"])
+
+    if args.debug:
+        debug_dir = os.path.join(results_dir, "debug_data")
+        os.makedirs(debug_dir, exist_ok=True)
+        os.makedirs(os.path.join(debug_dir, "videos/wrist_image/"), exist_ok=True)
+        os.makedirs(os.path.join(debug_dir, "videos/exterior_image_1_left/"), exist_ok=True)
+
+    instruction = None
+    while True:
+        if instruction is None:
+            instruction = input("Enter instruction: ")
+        else:
+            if input("Change instruction? (enter y or n) ").lower() == "y":
+                instruction = input("Enter instruction: ")
+
+        time.sleep(args.delay_seconds)
+
+        # Rollout parameters
+        actions_from_chunk_completed = 0
+        pred_action_chunk = None
+
+        # Prepare to save video of rollout
+        timestamp = datetime.datetime.now().strftime("%Y_%m_%d_%H:%M:%S")
+        video = []
+        if args.debug:
+            model_wrist_image_writer = imageio.get_writer(
+                os.path.join(
+                    debug_dir, "videos/wrist_image/", f"model_wrist_image_{timestamp}.mp4"
+                ),
+                fps=5,
+            )
+            model_exterior_image_1_left_writer = imageio.get_writer(
+                os.path.join(
+                    debug_dir,
+                    "videos/exterior_image_1_left/",
+                    f"model_exterior_image_1_left_{timestamp}.mp4",
+                ),
+                fps=5,
+            )
+
+        bar = tqdm.tqdm(range(args.max_timesteps))
+        print("Running rollout... press Ctrl+C to stop early.")
+
+        # Profiling variables (reset for each rollout)
+        rollout_start_time = time.time()
+        obs_times = deque(maxlen=50)  # Track observation collection times
+        server_times = deque(maxlen=50)  # Track server response times
+        action_count = 0
+        frame_buffer = deque(maxlen=video_history_len)
+
+        for t_step in bar:
+            step_start_time = time.time()
+            try:
+                # Get the current observation
+                obs_start_time = time.time()
+                curr_obs = _extract_observation(
+                    args,
+                    env.get_observation(),
+                    # Save the first observation to disk
+                    save_to_disk=t_step == 0,
+                )
+                obs_time = time.time() - obs_start_time
+                obs_times.append(obs_time)
+
+                video.append(curr_obs[f"{args.render_camera}_image"])
+
+                # Resize every step so the rolling frame buffer stays current.
+                left_image = resize_with_pad(curr_obs["left_image"], RESOLUTION[0], RESOLUTION[1])
+                right_image = resize_with_pad(curr_obs["right_image"], RESOLUTION[0], RESOLUTION[1])
+                wrist_image = resize_with_pad(curr_obs["wrist_image"], RESOLUTION[0], RESOLUTION[1])
+
+                if args.external_camera == "left":
+                    ext_image = left_image
+                elif args.external_camera == "right":
+                    ext_image = right_image
+
+                frame_buffer.append({"ext": ext_image, "wrist": wrist_image})
+
+                # Send websocket request to policy server if it's time to predict a new chunk
+                if (
+                    actions_from_chunk_completed == 0
+                    or actions_from_chunk_completed >= args.open_loop_horizon
+                ):
+                    actions_from_chunk_completed = 0
+
+                    if args.debug:
+                        model_wrist_image_writer.append_data(wrist_image)
+                        model_exterior_image_1_left_writer.append_data(ext_image)
+
+                    # Build video tensor with T frames derived from the model's
+                    # delta_indices (e.g. [-15, 0] -> T=2, [0] -> T=1).
+                    if video_T == 1:
+                        video_dict = {
+                            "exterior_image_1_left": ext_image[None, None, ...],
+                            "wrist_image_left": wrist_image[None, None, ...],
+                        }  # (B=1, T=1, H, W, C)
+                    else:
+                        hist_frame = frame_buffer[0]
+                        cur_frame = frame_buffer[-1]
+                        video_dict = {
+                            "exterior_image_1_left": np.stack(
+                                [hist_frame["ext"], cur_frame["ext"]]
+                            )[None, ...],
+                            "wrist_image_left": np.stack([hist_frame["wrist"], cur_frame["wrist"]])[
+                                None, ...
+                            ],
+                        }  # (B=1, T=video_T, H, W, C)
+
+                    # Build state dict from the model's reported state keys.
+                    state_dict = {}
+                    state_source = {
+                        "eef_9d": curr_obs["eef_9d"],
+                        "gripper_position": curr_obs["gripper_position"],
+                        "joint_position": curr_obs["joint_position"],
+                    }
+                    for key in state_keys:
+                        state_dict[key] = state_source[key][None, None, ...].astype(
+                            np.float32
+                        )  # (B=1, T=1, D)
+
+                    lang_key = modality_config["language"].modality_keys[0]
+                    request_data = {
+                        "video": video_dict,
+                        "state": state_dict,
+                        "language": {lang_key: [[instruction]]},
+                    }
+
+                    if args.vis_cameras:
+                        # viz the left image 1 and wrist image and use cv2 to display them side by side
+                        left_image_display = cv2.resize(
+                            left_image, (wrist_image.shape[1], wrist_image.shape[0])
+                        )
+                        combined_display = np.concatenate([left_image_display, wrist_image], axis=1)
+                        # convert to bgr
+                        combined_display = combined_display[..., ::-1]
+                        cv2.imshow("Camera Views", combined_display)
+                        cv2.waitKey(1)
+
+                    # Wrap the server call in a context manager to prevent Ctrl+C from interrupting it
+                    # Ctrl+C will be handled after the server call is complete
+                    server_start_time = time.time()
+                    with prevent_keyboard_interrupt():
+                        # this returns action chunk [N, 8] of joint position actions (7) + gripper position (1)
+                        response = policy_client.get_action(request_data)
+                    server_time = time.time() - server_start_time
+                    server_times.append(server_time)
+
+                    pred_action_chunk = np.concatenate(
+                        (
+                            response[0]["joint_position"][0],
+                            response[0]["gripper_position"][0],
+                        ),
+                        axis=1,
+                    )
+
+                # Select current action to execute from chunk
+                action = pred_action_chunk[actions_from_chunk_completed]
+                actions_from_chunk_completed += 1
+
+                # Binarize gripper action
+                if action[-1].item() > 0.5:
+                    action = np.concatenate([action[:-1], np.ones((1,))])
+                else:
+                    action = np.concatenate([action[:-1], np.zeros((1,))])
+
+                env.step(action)
+                action_count += 1
+
+                # Sleep to match DROID data collection frequency
+                elapsed_time = time.time() - step_start_time
+                if elapsed_time < 1 / DROID_CONTROL_FREQUENCY:
+                    time.sleep(1 / DROID_CONTROL_FREQUENCY - elapsed_time)
+
+                #  profiling stats
+                if obs_times:
+                    avg_obs_time = np.mean(obs_times) * 1000
+                    min_obs_time = np.min(obs_times) * 1000
+                    max_obs_time = np.max(obs_times) * 1000
+                else:
+                    avg_obs_time = min_obs_time = max_obs_time = 0
+
+                if server_times:
+                    avg_server_time = np.mean(server_times) * 1000
+                    min_server_time = np.min(server_times) * 1000
+                    max_server_time = np.max(server_times) * 1000
+                else:
+                    avg_server_time = min_server_time = max_server_time = 0
+
+                total_elapsed = time.time() - rollout_start_time
+                actions_per_sec = action_count / total_elapsed if total_elapsed > 0 else 0
+
+                bar.set_description(
+                    f"Obs: {avg_obs_time:.1f}ms [{min_obs_time:.1f}-{max_obs_time:.1f}] | "
+                    f"Server: {avg_server_time:.1f}ms [{min_server_time:.1f}-{max_server_time:.1f}] | "
+                    f"Actions/sec: {actions_per_sec:.2f}"
+                )
+            except KeyboardInterrupt:
+                break
+
+        os.makedirs(os.path.join(results_dir, "videos"), exist_ok=True)
+        video = np.stack(video)
+        # replace whitespace with underscores in instruction
+        sanitized_instruction = instruction.replace(" ", "_")
+        save_filename = os.path.join(
+            results_dir, "videos", f"{sanitized_instruction}_video_" + timestamp
+        )
+        with imageio.get_writer(save_filename + ".mp4", fps=args.render_fps) as writer:
+            for frame in video:
+                writer.append_data(frame)
+
+        if args.debug:
+            model_wrist_image_writer.close()
+            model_exterior_image_1_left_writer.close()
+
+        success: str | float | None = None
+        while not isinstance(success, float):
+            success = input(
+                "Did the rollout succeed? (enter y for 100%, n for 0%), or a numeric value 0-100 based on the evaluation spec"
+            )
+            if success == "y":
+                success = 1.0
+            elif success == "n":
+                success = 0.0
+
+            success = float(success) / 100
+            if not (0 <= success <= 1):
+                print(f"Success must be a number in [0, 100] but got: {success * 100}")
+
+        new_row = {
+            "success": success,
+            "duration": t_step,
+            "video_filename": save_filename,
+        }
+        new_index = len(df)
+        df.loc[new_index] = new_row
+
+        if input("Do one more eval? (enter y or n) ").lower() != "y":
+            break
+        env.reset(randomize=False)
+
+    timestamp = datetime.datetime.now().strftime("%I:%M%p_%B_%d_%Y")
+    csv_filename = os.path.join(results_dir, f"eval_{timestamp}.csv")
+    df.to_csv(csv_filename)
+    print(f"Results saved to {csv_filename}")
+
+
+def _extract_observation(args: Args, obs_dict, *, stereo_camera="left", save_to_disk=False):
+    image_observations = obs_dict["image"]
+    key_left = f"{args.left_camera_id}_{stereo_camera}"
+    key_right = f"{args.right_camera_id}_{stereo_camera}"
+    key_wrist = f"{args.wrist_camera_id}_{stereo_camera}"
+
+    left_image = image_observations.get(key_left)
+    right_image = image_observations.get(key_right)
+    wrist_image = image_observations.get(key_wrist)
+
+    available = list(image_observations.keys())
+    assert left_image is not None, (
+        f"Left camera not found for key {key_left!r}. Available keys: {available}. "
+        "Set --left-camera-id to the ZED serial used in observation keys."
+    )
+    assert right_image is not None, (
+        f"Right camera not found for key {key_right!r}. Available keys: {available}. "
+        "Set --right-camera-id to the ZED serial used in observation keys."
+    )
+    assert wrist_image is not None, (
+        f"Wrist camera not found for key {key_wrist!r}. Available keys: {available}. "
+        "Set --wrist-camera-id to the ZED serial used in observation keys."
+    )
+
+    # Drop the alpha dimension
+    left_image = left_image[..., :3]
+    right_image = right_image[..., :3]
+    wrist_image = wrist_image[..., :3]
+
+    # Convert to RGB
+    left_image = left_image[..., ::-1]
+    right_image = right_image[..., ::-1]
+    wrist_image = wrist_image[..., ::-1]
+
+    # In addition to image observations, also capture the proprioceptive state
+    robot_state = obs_dict["robot_state"]
+    cartesian_position = np.array(robot_state["cartesian_position"])
+    joint_position = np.array(robot_state["joint_positions"])
+    gripper_position = np.array([robot_state["gripper_position"]])
+    eef_9d = compute_eef_9d(cartesian_position)
+
+    # Save the images to disk so that they can be viewed live while the robot is running
+    # Create one combined image to make live viewing easy
+    if save_to_disk:
+        combined_image = np.concatenate([left_image, wrist_image, right_image], axis=1)
+        combined_image = Image.fromarray(combined_image)
+        combined_image.save("robot_camera_views.png")
+
+    return {
+        "left_image": left_image,
+        "right_image": right_image,
+        "wrist_image": wrist_image,
+        "cartesian_position": cartesian_position,
+        "eef_9d": eef_9d,
+        "joint_position": joint_position,
+        "gripper_position": gripper_position,
+    }
+
+
+if __name__ == "__main__":
+    args: Args = tyro.cli(Args)
+    main(args)

Isaac-GR00T/examples/DROID/server_client.py

@@ -0,0 +1,395 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import asdict, dataclass, is_dataclass
+from enum import Enum
+import io
+from typing import Any
+
+import msgpack
+import numpy as np
+import zmq
+
+
+def to_json_serializable(obj: Any) -> Any:
+    """
+    Recursively convert dataclasses and numpy arrays to JSON-serializable format.
+
+    Args:
+        obj: Object to convert (can be dataclass, numpy array, dict, list, etc.)
+
+    Returns:
+        JSON-serializable representation of the object
+    """
+    if is_dataclass(obj) and not isinstance(obj, type):
+        # Convert dataclass to dict, then recursively process the dict
+        return to_json_serializable(asdict(obj))
+    elif isinstance(obj, np.ndarray):
+        # Convert numpy array to list
+        return obj.tolist()
+    elif isinstance(obj, np.integer):
+        # Convert numpy integers to Python int
+        return int(obj)
+    elif isinstance(obj, np.floating):
+        # Convert numpy floats to Python float
+        return float(obj)
+    elif isinstance(obj, np.bool_):
+        # Convert numpy bool to Python bool
+        return bool(obj)
+    elif isinstance(obj, dict):
+        # Recursively process dictionary values
+        return {key: to_json_serializable(value) for key, value in obj.items()}
+    elif isinstance(obj, (list, tuple)):
+        # Recursively process list/tuple elements
+        return [to_json_serializable(item) for item in obj]
+    elif isinstance(obj, set):
+        # Convert set to list
+        return [to_json_serializable(item) for item in obj]
+    elif isinstance(obj, (str, int, float, bool, type(None))):
+        # Already JSON-serializable
+        return obj
+    elif isinstance(obj, Enum):
+        return obj.name
+    else:
+        # For other types, try to convert to string as fallback
+        # You might want to handle specific types differently
+        return str(obj)
+
+
+class MessageType(Enum):
+    START_OF_EPISODE = "start_of_episode"
+    END_OF_EPISODE = "end_of_episode"
+    EPISODE_STEP = "episode_step"
+    IMAGE = "image"
+    TEXT = "text"
+
+
+class ActionRepresentation(Enum):
+    RELATIVE = "relative"
+    DELTA = "delta"
+    ABSOLUTE = "absolute"
+
+
+class ActionType(Enum):
+    EEF = "eef"
+    NON_EEF = "non_eef"
+
+
+class ActionFormat(Enum):
+    DEFAULT = "default"
+    XYZ_ROT6D = "xyz+rot6d"
+    XYZ_ROTVEC = "xyz+rotvec"
+
+
+@dataclass
+class ActionConfig:
+    rep: ActionRepresentation
+    type: ActionType
+    format: ActionFormat
+    state_key: str | None = None
+
+
+@dataclass
+class ModalityConfig:
+    """Configuration for a modality defining how data should be sampled and loaded.
+
+    This class specifies which indices to sample relative to a base index and which
+    keys to load for a particular modality (e.g., video, state, action).
+    """
+
+    delta_indices: list[int]
+    """Delta indices to sample relative to the current index. The returned data will correspond to the original data at a sampled base index + delta indices."""
+    modality_keys: list[str]
+    """The keys to load for the modality in the dataset."""
+    sin_cos_embedding_keys: list[str] | None = None
+    """Optional list of keys to apply sin/cos encoding. If None or empty, use min/max normalization for all keys."""
+    mean_std_embedding_keys: list[str] | None = None
+    """Optional list of keys to apply mean/std normalization. If None or empty, use min/max normalization for all keys."""
+    action_configs: list[ActionConfig] | None = None
+
+    def __post_init__(self):
+        """Set default values for action-related fields if not specified."""
+        if self.action_configs is not None:
+            assert len(self.action_configs) == len(self.modality_keys), (
+                f"Number of action configs ({len(self.action_configs)}) must match number of modality keys ({len(self.modality_keys)})"
+            )
+            parsed_action_configs = []
+            for action_config in self.action_configs:
+                if isinstance(action_config, dict):
+                    action_config = ActionConfig(
+                        rep=ActionRepresentation[action_config["rep"]],
+                        type=ActionType[action_config["type"]],
+                        format=ActionFormat[action_config["format"]],
+                        state_key=action_config.get("state_key", None),
+                    )
+                parsed_action_configs.append(action_config)
+            self.action_configs = parsed_action_configs
+
+
+class MsgSerializer:
+    @staticmethod
+    def to_bytes(data: Any) -> bytes:
+        return msgpack.packb(data, default=MsgSerializer.encode_custom_classes)
+
+    @staticmethod
+    def from_bytes(data: bytes) -> Any:
+        return msgpack.unpackb(data, object_hook=MsgSerializer.decode_custom_classes)
+
+    @staticmethod
+    def decode_custom_classes(obj):
+        if not isinstance(obj, dict):
+            return obj
+        if "__ModalityConfig_class__" in obj:
+            return ModalityConfig(**obj["as_json"])
+        if "__ndarray_class__" in obj:
+            return np.load(io.BytesIO(obj["as_npy"]), allow_pickle=False)
+        return obj
+
+    @staticmethod
+    def encode_custom_classes(obj):
+        if isinstance(obj, ModalityConfig):
+            # Convert to dict and let msgpack recursively handle nested objects
+            return {"__ModalityConfig_class__": True, "as_json": to_json_serializable(obj)}
+        if isinstance(obj, np.ndarray):
+            output = io.BytesIO()
+            np.save(output, obj, allow_pickle=False)
+            return {"__ndarray_class__": True, "as_npy": output.getvalue()}
+        return obj
+
+
+class BasePolicy(ABC):
+    """Abstract base class for robotic control policies.
+
+    This class defines the interface that all policies must implement, including
+    methods for action computation, input/output validation, and state management.
+
+    Subclasses must implement:
+        - check_observation(): Validate observation format
+        - check_action(): Validate action format
+        - _get_action(): Core action computation logic
+        - reset(): Reset policy to initial state
+    """
+
+    def __init__(self, *, strict: bool = True):
+        self.strict = strict
+
+    @abstractmethod
+    def check_observation(self, observation: dict[str, Any]) -> None:
+        """Check if the observation is valid.
+
+        Args:
+            observation: Dictionary containing the current state/observation of the environment
+
+        Raises:
+            AssertionError: If the observation is invalid.
+        """
+        pass
+
+    @abstractmethod
+    def check_action(self, action: dict[str, Any]) -> None:
+        """Check if the action is valid.
+
+        Args:
+            action: Dictionary containing the action to be executed
+
+        Raises:
+            AssertionError: If the action is invalid.
+        """
+        pass
+
+    @abstractmethod
+    def _get_action(
+        self, observation: dict[str, Any], options: dict[str, Any] | None = None
+    ) -> tuple[dict[str, Any], dict[str, Any]]:
+        """Compute and return the next action based on current observation.
+
+        This method should be overridden by subclasses to implement policy-specific
+        action computation. Input validation is handled by the public get_action() method.
+
+        Args:
+            observation: Dictionary containing the current state/observation
+            options: Optional configuration dict for action computation
+
+        Returns:
+            Tuple of (action, info):
+                - action: Dictionary containing the action to be executed
+                - info: Dictionary containing additional metadata (e.g., confidence scores)
+        """
+        pass
+
+    def get_action(
+        self, observation: dict[str, Any], options: dict[str, Any] | None = None
+    ) -> tuple[dict[str, Any], dict[str, Any]]:
+        """Compute and return the next action based on current observation with validation.
+
+        This is the main public interface. It validates the observation, calls
+        the internal _get_action(), and validates the resulting action.
+
+        Args:
+            observation: Dictionary containing the current state/observation
+            options: Optional configuration dict for action computation
+
+        Returns:
+            Tuple of (action, info):
+                - action: Dictionary containing the validated action
+                - info: Dictionary containing additional metadata
+
+        Raises:
+            AssertionError/ValueError: If observation or action validation fails
+        """
+        if self.strict:
+            self.check_observation(observation)
+        action, info = self._get_action(observation, options)
+        if self.strict:
+            self.check_action(action)
+        return action, info
+
+    @abstractmethod
+    def reset(self, options: dict[str, Any] | None = None) -> dict[str, Any]:
+        """Reset the policy to its initial state.
+
+        Args:
+            options: Dictionary containing the options for the reset
+
+        Returns:
+            Dictionary containing the info after resetting the policy
+        """
+        pass
+
+
+class PolicyClient(BasePolicy):
+    def __init__(
+        self,
+        host: str = "localhost",
+        port: int = 5555,
+        timeout_ms: int = 15000,
+        api_token: str = None,
+        strict: bool = False,
+    ):
+        super().__init__(strict=strict)
+        self._closed = False
+        self.context = zmq.Context()
+        self.host = host
+        self.port = port
+        self.timeout_ms = timeout_ms
+        self.api_token = api_token
+        self._init_socket()
+
+    def _init_socket(self):
+        """Initialize or reinitialize the socket with current settings"""
+        self.socket = self.context.socket(zmq.REQ)
+        self.socket.connect(f"tcp://{self.host}:{self.port}")
+
+    def ping(self) -> bool:
+        try:
+            self.call_endpoint("ping", requires_input=False)
+            return True
+        except zmq.error.ZMQError:
+            self._init_socket()  # Recreate socket for next attempt
+            return False
+
+    def kill_server(self):
+        """
+        Kill the server.
+        """
+        self.call_endpoint("kill", requires_input=False)
+
+    def call_endpoint(
+        self, endpoint: str, data: dict | None = None, requires_input: bool = True
+    ) -> Any:
+        """
+        Call an endpoint on the server.
+
+        Args:
+            endpoint: The name of the endpoint.
+            data: The input data for the endpoint.
+            requires_input: Whether the endpoint requires input data.
+        """
+        request: dict = {"endpoint": endpoint}
+        if requires_input:
+            request["data"] = data
+        if self.api_token:
+            request["api_token"] = self.api_token
+
+        self.socket.send(MsgSerializer.to_bytes(request))
+        message = self.socket.recv()
+        if message == b"ERROR":
+            raise RuntimeError("Server error. Make sure we are running the correct policy server.")
+        response = MsgSerializer.from_bytes(message)
+
+        if isinstance(response, dict) and "error" in response:
+            raise RuntimeError(f"Server error: {response['error']}")
+        return response
+
+    def close(self) -> None:
+        """Release the REQ socket and ZMQ context. Idempotent.
+
+        Mirrors ``gr00t.policy.server_client.PolicyClient.close()`` so the
+        DROID example demonstrates the explicit-lifecycle pattern.
+        """
+        if getattr(self, "_closed", True):
+            return
+        self._closed = True
+        socket = getattr(self, "socket", None)
+        if socket is not None:
+            try:
+                socket.close(linger=0)
+            except Exception:
+                pass
+        context = getattr(self, "context", None)
+        if context is not None:
+            try:
+                context.term()
+            except Exception:
+                pass
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc, tb):
+        self.close()
+
+    def __del__(self):
+        try:
+            self.close()
+        except Exception:
+            pass
+
+    def _get_action(
+        self, observation: dict[str, Any], options: dict[str, Any] | None = None
+    ) -> tuple[dict[str, Any], dict[str, Any]]:
+        response = self.call_endpoint(
+            "get_action", {"observation": observation, "options": options}
+        )
+        return tuple(response)  # Convert list (from msgpack) to tuple of (action, info)
+
+    def reset(self, options: dict[str, Any] | None = None) -> dict[str, Any]:
+        return self.call_endpoint("reset", {"options": options})
+
+    def get_modality_config(self) -> dict[str, ModalityConfig]:
+        return self.call_endpoint("get_modality_config", requires_input=False)
+
+    def check_observation(self, observation: dict[str, Any]) -> None:
+        raise NotImplementedError(
+            "check_observation is not implemented. Please use `strict=False` to disable strict mode or implement this method in the subclass."
+        )
+
+    def check_action(self, action: dict[str, Any]) -> None:
+        raise NotImplementedError(
+            "check_action is not implemented. Please use `strict=False` to disable strict mode or implement this method in the subclass."
+        )

Isaac-GR00T/examples/DROID/utils.py

@@ -0,0 +1,81 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Taken from https://github.com/Physical-Intelligence/openpi/tree/main/packages/openpi-client/src/openpi_client
+"""
+
+import numpy as np
+from PIL import Image
+
+
+def convert_to_uint8(img: np.ndarray) -> np.ndarray:
+    """Converts an image to uint8 if it is a float image.
+
+    This is important for reducing the size of the image when sending it over the network.
+    """
+    if np.issubdtype(img.dtype, np.floating):
+        img = (255 * img).astype(np.uint8)
+    return img
+
+
+def resize_with_pad(
+    images: np.ndarray, height: int, width: int, method=Image.BILINEAR
+) -> np.ndarray:
+    """Replicates tf.image.resize_with_pad for multiple images using PIL. Resizes a batch of images to a target height.
+
+    Args:
+        images: A batch of images in [..., height, width, channel] format.
+        height: The target height of the image.
+        width: The target width of the image.
+        method: The interpolation method to use. Default is bilinear.
+
+    Returns:
+        The resized images in [..., height, width, channel].
+    """
+    # If the images are already the correct size, return them as is.
+    if images.shape[-3:-1] == (height, width):
+        return images
+
+    original_shape = images.shape
+
+    images = images.reshape(-1, *original_shape[-3:])
+    resized = np.stack(
+        [_resize_with_pad_pil(Image.fromarray(im), height, width, method=method) for im in images]
+    )
+    return resized.reshape(*original_shape[:-3], *resized.shape[-3:])
+
+
+def _resize_with_pad_pil(image: Image.Image, height: int, width: int, method: int) -> Image.Image:
+    """Replicates tf.image.resize_with_pad for one image using PIL. Resizes an image to a target height and
+    width without distortion by padding with zeros.
+
+    Unlike the jax version, note that PIL uses [width, height, channel] ordering instead of [batch, h, w, c].
+    """
+    cur_width, cur_height = image.size
+    if cur_width == width and cur_height == height:
+        return image  # No need to resize if the image is already the correct size.
+
+    ratio = max(cur_width / width, cur_height / height)
+    resized_height = int(cur_height / ratio)
+    resized_width = int(cur_width / ratio)
+    resized_image = image.resize((resized_width, resized_height), resample=method)
+
+    zero_image = Image.new(resized_image.mode, (width, height), 0)
+    pad_height = max(0, int((height - resized_height) / 2))
+    pad_width = max(0, int((width - resized_width) / 2))
+    zero_image.paste(resized_image, (pad_width, pad_height))
+    assert zero_image.size == (width, height)
+    return zero_image

Isaac-GR00T/examples/GR00TWholeBodyControl/README.md

@@ -0,0 +1,115 @@
+# GR00T Whole-Body Control Real-World Benchmark
+
+This example reports a real-world Unitree G1 evaluation using **Isaac GR00T N1.7** together with **GR00T Whole-Body Control / GEAR-SONIC**. The benchmark focuses on everyday mobile-manipulation tasks that require walking, table approach, grasping, foot placement, and whole-body pickup motions.
+
+## Summary
+
+GR00T N1.7 with SONIC can execute closed-loop whole-body skills on a real humanoid robot. The main targeted task is **walk to a table and pick up an object**. A single mixed-object policy was trained from demonstrations covering about 50 table-top objects, then evaluated systematically across 8 representative objects in the training set. This follows the object-pickup setting studied in the [SONIC paper](https://nvlabs.github.io/GEAR-SONIC/static/pdf/sonic_paper.pdf), where the robot walks to a table, locates a target object, and grasps it under randomized table heights and object positions. A second task-specific policy was trained for walking to a small table, picking up a soda can, stepping on a trash-can trigger, and dropping the can inside.
+
+For more GR00T Whole-Body Control task examples, see the [GEAR-SONIC project page](https://nvlabs.github.io/GEAR-SONIC/).
+
+## Evaluation Results
+
+| Task Family | Policy Setup | Task Specification | Logged Trials | Successes | Success Rate |
+| --- | --- | --- | ---: | ---: | ---: |
+| Walk to table and pick up object | Single mixed-object pickup policy | Walk to the table, localize a target object, grasp it, and lift it. Evaluated on 8 objects across 3 table heights and 9 object placements. Autonomous retries count as success. | 216 | 159 | 73.6% |
+| Soda can from small table to trash can | Task-specific soda-can policy | Walk toward a small table, pick up a soda can, rotate toward the trash can, step on the trigger, and drop the can inside. | 12 | 8 | 66.7% |
+
+For object pickup, each object was evaluated with 27 trials: 3 table heights (24, 27, and 30 inches) crossed with a 3 by 3 grid of object placements on the table (left/middle/right and front/middle/back). The failures were mostly grasp failures: the gripper missed the object, contacted it from an unstable grasp point, pushed it out of reach, or dropped it into a pose that the policy could not recover from. The soda-can failures were similarly dominated by missed grasps, unstable grasps, or missed trigger steps.
+
+### Object Pickup Breakdown
+
+| Object | Trials | Successes | Success Rate |
+| --- | ---: | ---: | ---: |
+| Towel | 27 | 24 | 88.9% |
+| Shoe | 27 | 14 | 51.9% |
+| Apple | 27 | 19 | 70.4% |
+| Scoop | 27 | 18 | 66.7% |
+| Lamp | 27 | 17 | 63.0% |
+| Flashlight | 27 | 20 | 74.1% |
+| Fruit | 27 | 26 | 96.3% |
+| Sock | 27 | 21 | 77.8% |
+
+## Demo Videos
+
+The MP4 samples below are attached as video files in this repository. Click any preview image to open the corresponding MP4. The source tree intentionally keeps only a curated sample set; full per-trial review videos are better shared as supplementary material through a stable gallery or archive. For more task demos beyond these two quantified results, see the [GEAR-SONIC project page](https://nvlabs.github.io/GEAR-SONIC/).
+
+### Walk To Table And Pick Up Objects
+
+Representative examples are included for the evaluated object pickup task. Captions describe the behavior visible in the clip; some successful clips include retries, regrasping, object contact, or small recovery motions because autonomous recovery was counted as success during evaluation. A trial is considered failure when it stucks for over 30s without progress.
+
+| Object | Example 1 | Example 2 |
+| --- | --- | --- |
+| Lamp | [<img src="media/g1_real_eval/posters/mixed_pickup_lamp_01_pickup_success.jpg" alt="Lamp pickup from a 24-inch table placement" width="280">](media/g1_real_eval/videos/mixed_pickup_lamp_01_pickup_success.mp4)<br>Picks up the lamp from a 24-inch table | [<img src="media/g1_real_eval/posters/mixed_pickup_lamp_02_base_grasp_success.jpg" alt="Lamp base grasp after contact at a 30-inch table placement" width="280">](media/g1_real_eval/videos/mixed_pickup_lamp_02_base_grasp_success.mp4)<br>Contacts the lamp, then grasps the base |
+| Towel | [<img src="media/g1_real_eval/posters/mixed_pickup_towel_01_pickup_success.jpg" alt="Towel pickup from a 24-inch table placement" width="280">](media/g1_real_eval/videos/mixed_pickup_towel_01_pickup_success.mp4)<br>Picks up the towel | [<img src="media/g1_real_eval/posters/mixed_pickup_towel_02_regrasp_success.jpg" alt="Towel grasp, drop, and regrasp sequence" width="280">](media/g1_real_eval/videos/mixed_pickup_towel_02_regrasp_success.mp4)<br>Grasps, drops, and regrasps |
+| Apple | [<img src="media/g1_real_eval/posters/mixed_pickup_apple_01_retry_success.jpg" alt="Apple pickup after a retry grasp" width="280">](media/g1_real_eval/videos/mixed_pickup_apple_01_retry_success.mp4)<br>Retries the grasp and succeeds | [<img src="media/g1_real_eval/posters/mixed_pickup_apple_02_adjust_success.jpg" alt="Apple pickup after gripper adjustment over the object" width="280">](media/g1_real_eval/videos/mixed_pickup_apple_02_adjust_success.mp4)<br>Adjusts over the apple before lifting |
+| Shoe | [<img src="media/g1_real_eval/posters/mixed_pickup_shoe_01_many_attempts_success.jpg" alt="Shoe pickup after multiple grasp attempts" width="280">](media/g1_real_eval/videos/mixed_pickup_shoe_01_many_attempts_success.mp4)<br>Succeeds after several attempts | [<img src="media/g1_real_eval/posters/mixed_pickup_shoe_02_regrasp_success.jpg" alt="Shoe drop and regrasp success" width="280">](media/g1_real_eval/videos/mixed_pickup_shoe_02_regrasp_success.mp4)<br>Drops the shoe, then regrasps |
+| Scoop | [<img src="media/g1_real_eval/posters/mixed_pickup_scoop_01_pickup_success.jpg" alt="Scoop pickup from a 27-inch table placement" width="280">](media/g1_real_eval/videos/mixed_pickup_scoop_01_pickup_success.mp4)<br>Picks up the scoop | [<img src="media/g1_real_eval/posters/mixed_pickup_scoop_02_noisy_success.jpg" alt="Scoop pickup with noisy approach motion" width="280">](media/g1_real_eval/videos/mixed_pickup_scoop_02_noisy_success.mp4)<br>Noisy approach, successful pickup |
+| Flashlight | [<img src="media/g1_real_eval/posters/mixed_pickup_flashlight_01_smooth_success.jpg" alt="Smooth flashlight pickup" width="280">](media/g1_real_eval/videos/mixed_pickup_flashlight_01_smooth_success.mp4)<br>Smooth pickup | [<img src="media/g1_real_eval/posters/mixed_pickup_flashlight_02_pickup_success.jpg" alt="Flashlight pickup from a 27-inch table placement" width="280">](media/g1_real_eval/videos/mixed_pickup_flashlight_02_pickup_success.mp4)<br>Picks up the flashlight |
+| Fruit | [<img src="media/g1_real_eval/posters/mixed_pickup_fruit_01_pickup_success.jpg" alt="Fruit pickup from a 24-inch table placement" width="280">](media/g1_real_eval/videos/mixed_pickup_fruit_01_pickup_success.mp4)<br>Picks up the fruit | [<img src="media/g1_real_eval/posters/mixed_pickup_fruit_02_regrasp_success.jpg" alt="Fruit grasp, drop, and regrasp success" width="280">](media/g1_real_eval/videos/mixed_pickup_fruit_02_regrasp_success.mp4)<br>Grasps, drops, and regrasps |
+| Sock | [<img src="media/g1_real_eval/posters/mixed_pickup_sock_01_top_grasp_success.jpg" alt="Sock top-down grasp success" width="280">](media/g1_real_eval/videos/mixed_pickup_sock_01_top_grasp_success.mp4)<br>Top-down grasp succeeds | [<img src="media/g1_real_eval/posters/mixed_pickup_sock_02_regrasp_success.jpg" alt="Sock grasp, drop, and regrasp success" width="280">](media/g1_real_eval/videos/mixed_pickup_sock_02_regrasp_success.mp4)<br>Drops once, then regrasps |
+
+### Soda Can From Small Table To Trash Can
+
+| Task | Example 1 | Example 2 | Example 3 |
+| --- | --- | --- | --- |
+| Soda can to trash | [<img src="media/g1_real_eval/posters/soda_can_table_trash_01_smooth_success.jpg" alt="Soda can picked up and dropped into the trash can in one smooth sequence" width="240">](media/g1_real_eval/videos/soda_can_table_trash_01_smooth_success.mp4)<br>Smooth full sequence | [<img src="media/g1_real_eval/posters/soda_can_table_trash_02_second_grasp_step_success.jpg" alt="Soda can succeeds after a second grasp and second trigger step" width="240">](media/g1_real_eval/videos/soda_can_table_trash_02_second_grasp_step_success.mp4)<br>Second grasp and trigger step | [<img src="media/g1_real_eval/posters/soda_can_table_trash_03_clean_success.jpg" alt="Soda can picked up and dropped into the trash can without a visible retry" width="240">](media/g1_real_eval/videos/soda_can_table_trash_03_clean_success.mp4)<br>Single-attempt full sequence |
+
+## Data Collection Experience
+
+We followed the [GR00T Whole-Body Control data collection tutorial](https://nvlabs.github.io/GR00T-WholeBodyControl/tutorials/data_collection.html) to collect G1 whole-body manipulation datasets.
+
+Training a good policy depends on both collecting high-quality data and configuring training properly. Each demonstration should ideally complete the task on the first attempt, without corrective motions such as re-grasping after a failed grasp or redoing a missed stepping trigger. For the mixed object pickup policy, we trained for 60k iterations at batch size 256 on roughly 18k episodes covering about 50 different objects; for the soda-can-to-trash policy, we trained for 20k iterations at batch size 32 on roughly 150 episodes of this single task.
+
+### Notes
+
+1. The mixed pickup result uses one policy trained from a combined object dataset and evaluated across multiple table-top objects.
+2. For the pickup task, we used an in-house UMI gripper design. Similar tasks may require users to build task-appropriate gripper designs for their own hardware setup.
+
+## Data-Train-Eval Workflow
+
+This benchmark used the public GR00T N1.7 and GR00T Whole-Body Control workflow:
+
+1. Collect G1 demonstrations with SONIC teleoperation.
+2. Fine-tune GR00T N1.7 with `UNITREE_G1_SONIC`.
+3. Run the GR00T policy server and the SONIC robot-side controller.
+4. Evaluate in closed loop on the real robot with video recording.
+
+Fine-tuning used the SONIC embodiment tag:
+
+```bash
+bash examples/finetune.sh \
+  --base-model-path nvidia/GR00T-N1.7-3B \
+  --dataset-path /path/to/your/lerobot_dataset \
+  --embodiment-tag UNITREE_G1_SONIC \
+  --output-dir /path/to/output_checkpoint \
+  --experiment-name g1-sonic-task
+```
+
+Closed-loop evaluation used a GR00T policy server:
+
+```bash
+python gr00t/eval/run_gr00t_server.py \
+  --model-path /path/to/output_checkpoint/checkpoint-<step> \
+  --embodiment-tag UNITREE_G1_SONIC \
+  --device cuda:0 \
+  --host 0.0.0.0 \
+  --port 5550
+```
+
+The robot-side controller follows the GR00T Whole-Body Control VLA inference workflow.
+
+### Notes
+
+- The data format should be LeRobot V2 already and ready to train the model.
+- It would be beneficial to check open-loop robot joint trajectory matching against training set/validation set before deploying on real robot.
+- As suggested in Data Collection Experience, data quality is preferred than quantity during post-training.
+
+## References
+
+- [NVIDIA Isaac GR00T](https://github.com/NVIDIA/Isaac-GR00T)
+- [SONIC paper](https://nvlabs.github.io/GEAR-SONIC/static/pdf/sonic_paper.pdf)
+- [GR00T Whole-Body Control documentation](https://nvlabs.github.io/GR00T-WholeBodyControl/)
+- [GEAR-SONIC project page](https://nvlabs.github.io/GEAR-SONIC/)
+- [GR00T data preparation guide](../../getting_started/data_preparation.md)
+- [GR00T policy server/client guide](../../getting_started/policy.md)