owhan/PolaRiS-Hub

Dataset

PolaRiS Hub

4

16 commits

2 linked in READMEs

updated Mar 14, 2026

See the code

README

PolaRiS Hub

PolaRiS Hub is a lightweight collection of reconstructed real-to-sim manipulation environments used for evaluating generalist robot policies in simulation. It is designed to be used with the PolaRiS evaluation framework.


Instructions on how to use can be found at the Github PolaRiS Codebase

What’s in the Hub

Each environment in PolaRiS Hub includes:

  • A reconstructed scene (mesh + Gaussian splats)
  • Canonical initial conditions
  • A language instruction describing the task
  • Task-specific evaluation metadata (success & progress)

The full hub is currently < 2GB and can be downloaded in a single command.


Available Environments

Environment NameTask Description
DROID-BlockStackKitchenPlace and stack the blocks on top of the green tray
DROID-FoodBussingPut all the foods in the bowl
DROID-PanCleanUse the yellow sponge to scrub the blue-handle frying pan
DROID-MoveLatteCupPut the latte art cup on top of the cutting board
DROID-OrganizeToolsPut the scissor into the large container
DROID-TapeIntoContainerPut the tape into the container

Download

uvx hf download owhan/PolaRiS-Hub --repo-type=dataset --local-dir ./PolaRiS-Hub

Intended Use

  • Evaluation of generalist robot policies
  • Real-to-sim benchmarking
  • Zero-shot task evaluation in simulation

Contributing

Please consider contributing your environments for broader use by the community, and to grow the means of generalist policy evaluation! See Github codebase on instruction for contributing.


Citation

@misc{jain2025polarisscalablerealtosimevaluations,
      title={PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies}, 
      author={Arhan Jain and Mingtong Zhang and Kanav Arora and William Chen and Marcel Torne and Muhammad Zubair Irshad and Sergey Zakharov and Yue Wang and Sergey Levine and Chelsea Finn and Wei-Chiu Ma and Dhruv Shah and Abhishek Gupta and Karl Pertsch},
      year={2025},
      eprint={2512.16881},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2512.16881}, 
}

owhan/PolaRiS-Hub

Dataset

PolaRiS Hub

4

16 commits

2 linked in READMEs

updated Mar 14, 2026

See the code

README

PolaRiS Hub

PolaRiS Hub is a lightweight collection of reconstructed real-to-sim manipulation environments used for evaluating generalist robot policies in simulation. It is designed to be used with the PolaRiS evaluation framework.


Instructions on how to use can be found at the Github PolaRiS Codebase

What’s in the Hub

Each environment in PolaRiS Hub includes:

  • A reconstructed scene (mesh + Gaussian splats)
  • Canonical initial conditions
  • A language instruction describing the task
  • Task-specific evaluation metadata (success & progress)

The full hub is currently < 2GB and can be downloaded in a single command.


Available Environments

Environment NameTask Description
DROID-BlockStackKitchenPlace and stack the blocks on top of the green tray
DROID-FoodBussingPut all the foods in the bowl
DROID-PanCleanUse the yellow sponge to scrub the blue-handle frying pan
DROID-MoveLatteCupPut the latte art cup on top of the cutting board
DROID-OrganizeToolsPut the scissor into the large container
DROID-TapeIntoContainerPut the tape into the container

Download

uvx hf download owhan/PolaRiS-Hub --repo-type=dataset --local-dir ./PolaRiS-Hub

Intended Use

  • Evaluation of generalist robot policies
  • Real-to-sim benchmarking
  • Zero-shot task evaluation in simulation

Contributing

Please consider contributing your environments for broader use by the community, and to grow the means of generalist policy evaluation! See Github codebase on instruction for contributing.


Citation

@misc{jain2025polarisscalablerealtosimevaluations,
      title={PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies}, 
      author={Arhan Jain and Mingtong Zhang and Kanav Arora and William Chen and Marcel Torne and Muhammad Zubair Irshad and Sergey Zakharov and Yue Wang and Sergey Levine and Chelsea Finn and Wei-Chiu Ma and Dhruv Shah and Abhishek Gupta and Karl Pertsch},
      year={2025},
      eprint={2512.16881},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2512.16881}, 
}