Datasets:
pretty_name: MAP-Bench
language:
- en
license: other
license_name: hm3d-derived-benchmark-terms
license_link: LICENSE.md
task_categories:
- robotics
size_categories:
- 10K<n<100K
viewer: false
tags:
- robotics
- navigation
- benchmark
- map-bench
- hm3d
MAP-Bench
Paper · Project · GitHub · Checkpoints
MAP-Bench evaluates instruction-conditioned navigation to a Manipulation Anchor Pose: the base position and heading from which a robot can begin the intended manipulation. Built in HM3D, it uses one forward RGB view and three distance levels: Short, Medium, and Long.
- 35,454 training trajectories with RGB observations, navigation actions, instructions, target boxes, and MAP annotations.
- 15,000 evaluation episodes with scene references, start poses, target MAPs, and instructions.
- 75,000 evaluation rollouts and their results: 15,000 each for GR00T N1.7, π₀.₅, FastWAM, UniWAM-Base, and UniWAM-Scaled.
Download
Run from your workspace directory to download the complete benchmark package (approximately 18.3 GB before extraction):
python -m pip install -U huggingface_hub
hf download Fysics-AI/MAP-Bench --repo-type dataset --local-dir ./MAP-Bench
Training videos and evaluation rollouts are stored in compressed archives. Use the GitHub download helper with --training --traces to verify and extract them. Obtain HM3D scene assets separately for simulator evaluation.
Usage
See GitHub for benchmark setup and evaluation, navigation training, and results.
HM3D-derived data are subject to the source dataset terms; see LICENSE.md.