maum-ai/CostNav-Teleop-Dataset

CostNav Teleop Dataset

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Dataset Summary

The CostNav Teleop Dataset is a large-scale collection of human teleoperation recordings for robot navigation in an urban sidewalk simulation environment. It was collected as part of the CostNav benchmark, which evaluates navigation systems using real-world economic cost and revenue metrics rather than purely technical metrics.

The dataset contains 2,203 teleoperation episodes totaling 50.2 hours of driving data, collected by 4 human operators using joystick control of a Segway E1 delivery robot in NVIDIA Isaac Sim. Each episode is stored as a ROS 2 bag file in MCAP format with synchronized multi-modal sensor data.

Navigation Comparison

Physics Simulation

CostNav's high-fidelity physics simulation enables the modeling of real-world economic scenarios, including critical failures like food spoilage and robot rollovers.

Economic Model

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Key Statistics

Statistic	Value
Total episodes	2,203
Total duration	50.2 hours
Total ROS messages	~71.4M
Collectors	4 (A, B, C, D)
Episodes per collector	A: 376, B: 744, C: 73, D: 446
Episode outcomes	Success (O): 1,639 / Partial (△): 59 / Fail (X): 505
Food camera available	Yes: 1,136 / No: 1,067
Storage format	ROS 2 bag (MCAP)
Simulation platform	NVIDIA Isaac Sim
Robot	Segway E1 delivery robot
ROS distribution	Jazzy

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Dataset Structure

CostNav-Teleop-Dataset/
├── collector_A/                          # 376 episodes
│   ├── recording_YYYYMMDD_HHMMSS/
│   │   ├── metadata.yaml                 # ROS bag metadata (topics, QoS, message counts)
│   │   └── recording_*_0.mcap            # MCAP bag file with all sensor data
│   └── ...
├── collector_B/                          # 744 episodes
├── collector_C/                          # 73 episodes
├── collector_D/                          # 446 episodes
└── costnav data collection_v3 recording - rosbags_time_summary.csv

Summary CSV Columns

Column	Description
recording_dir	Path to recording directory
metadata_path	Path to metadata YAML
mcap_files	MCAP bag filename
start_time_utc / end_time_utc	Episode timestamps (UTC)
start_time_kst / end_time_kst	Episode timestamps (KST)
duration_seconds	Episode duration in seconds
duration_hms	Episode duration (H:MM:SS)
message_count	Total ROS messages in episode
episode_success	Outcome: O (success), △ (partial), X (fail)
food_cam	Whether food camera data is included (O/X)

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ROS 2 Topics

Each MCAP recording contains the following topics:

Topic	Message Type	Description
/front_stereo_camera/left/image_raw/compressed	sensor_msgs/CompressedImage	Front stereo camera (left, compressed)
/front_stereo_camera/left/camera_info	sensor_msgs/CameraInfo	Camera intrinsics
/front_3d_lidar/lidar_points	sensor_msgs/PointCloud2	3D LiDAR point cloud
/scan	sensor_msgs/LaserScan	2D laser scan
/chassis/odom	nav_msgs/Odometry	Wheel odometry
/chassis/imu	sensor_msgs/Imu	Chassis IMU
/front_stereo_imu/imu	sensor_msgs/Imu	Stereo camera IMU
/cmd_vel	geometry_msgs/Twist	Teleop velocity commands
/joy	sensor_msgs/Joy	Raw joystick input
/is_model	std_msgs/Bool	Model control flag
/tf / /tf_static	tf2_msgs/TFMessage	Transform tree
/map	nav_msgs/OccupancyGrid	Occupancy grid map
/goal_pose	geometry_msgs/PoseStamped	Goal position
/initialpose	geometry_msgs/PoseWithCovarianceStamped	Initial pose estimate
/start_marker / /goal_marker / /robot_marker	visualization_msgs/Marker	RViz markers
/clock	rosgraph_msgs/Clock	Simulation clock
/diagnostics	diagnostic_msgs/DiagnosticArray	System diagnostics

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Usage

Loading with MCAP

from mcap.reader import make_reader

with open("collector_A/recording_20260202_052239/recording_20260202_052239_0.mcap", "rb") as f:
    reader = make_reader(f)
    for schema, channel, message in reader.iter_messages():
        print(f"{channel.topic}: {schema.name} @ {message.log_time}")

Loading with ROS 2

# Play back a recording
ros2 bag play collector_A/recording_20260202_052239/

# Inspect bag info
ros2 bag info collector_A/recording_20260202_052239/

Data Processing: MCAP to Training Format

The CostNav repository includes a full data processing pipeline to convert raw MCAP bags into training-ready formats for imitation learning baselines (ViNT, NavDP, etc.).

Step 1: Convert MCAP bags to MediaRef format (extracts images to H.264 video for efficient lazy-loading):

python costnav_isaacsim/il_training/data_processing/converters/rosbag_to_mediaref.py \
    --input data/sample_rosbags/recording_20260109_061808 \
    --output data/processed/recording_20260109_061808

Step 2: Batch convert multiple bags in parallel using Ray:

python costnav_isaacsim/il_training/data_processing/converters/ray_batch_convert.py \
    --input-dir data/sample_rosbags/ \
    --output-dir data/mediaref/ \
    --num-workers 4

Step 3: Convert MediaRef to ViNT training format (synchronized images + odometry at configurable Hz):

python costnav_isaacsim/il_training/data_processing/process_data/process_mediaref_bags.py \
    --input-dir data/mediaref/ \
    --output-dir data/vint_format/ \
    --config costnav_isaacsim/il_training/data_processing/configs/vint_processing_config.yaml

Output structure (ViNT format):

data/vint_format/
└── trajectory_name_0/
    ├── 0.jpg, 1.jpg, ...    # Extracted images (160x120, 4:3)
    └── traj_data.pkl         # Position and yaw arrays

Alternative: Convert to NavDP format (with depth estimation via DepthAnythingV2):

python costnav_isaacsim/il_training/data_processing/process_data/process_mediaref_to_navdp.py \
    --input-dir data/mediaref/ \
    --output-dir data/navdp_format/ \
    --config costnav_isaacsim/il_training/data_processing/configs/navdp_processing_config.yaml

See the full Data Processing Documentation and Teleoperation Guide for details on configuration, the recording pipeline, and robot control setup.

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Related Resources

• CostNav GitHub Repository - Benchmark code, simulation environment, and evaluation tools
• CostNav Project Page - Overview and results
• CostNav Documentation - Full setup and usage guides
• Blog Post - Summary and motivation
• Cost Model (Google Drive) - Full cost benchmark formula with real-world references

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Citation

If you use this dataset in your research, please cite the CostNav paper:

@misc{seong2026costnavnavigationbenchmarkrealworld,
      title={CostNav: A Navigation Benchmark for Real-World Economic-Cost Evaluation of Physical AI Agents},
      author={Haebin Seong and Sungmin Kim and Yongjun Cho and Myunchul Joe and Geunwoo Kim and Yubeen Park and Sunhoo Kim and Yoonshik Kim and Suhwan Choi and Jaeyoon Jung and Jiyong Youn and Jinmyung Kwak and Sunghee Ahn and Jaemin Lee and Younggil Do and Seungyeop Yi and Woojin Cheong and Minhyeok Oh and Minchan Kim and Seongjae Kang and Samwoo Seong and Youngjae Yu and Yunsung Lee},
      year={2026},
      eprint={2511.20216},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2511.20216},
}

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License

This dataset is released under the Apache 2.0 License.