NeurIPS 2023 HomeRobot: Open Vocabulary Mobile Manipulation (OVMM) Challenge

Stage 1: Simulation 1

Below are example scenes from the simulation benchmark. Participants do not require a robot to compete, instead performing entirely in simulation. We will then take the best performing simulation systems and run them on our held-out physical hardware environment (see blow)

The environments all contain multiple rooms with varying layounts. An example top-down layout is presented on the right. For the examples here, we have removed small objects and clutter that will be present in the actual evaluation. See github.com/facebookresearch/home-robot for training agents.

Stage 2: Physical Robot 2

Once systems have been tested in simulation, we will transfer the most competitive entries to a physical robot. Below you can see part of the physical robot evaluation environment hosted by Meta in Fremont California. The unseen evaluation settings will change or introduce furniture, object locations, and object types (not pictured).

The software stack is designed to be easy to use and has therefore also been tested in university labs. We invite you to try the stack even if you do not plan to submit to the challenge. To help advance robotics research and fair benchmarking, our stack is designed to quickly on-board new researchers into robotics research. Everything is written in python with no knowledge of ROS required. The full open-source software stack is available at github.com/facebookresearch/home-robot.

Stage 1: Simulation 1

We will use EvalAI 3 to host the challenge. Participants should register for the competition on the EvalAI challenge page and create a team. Participants need to upload docker containers with their agents.

The challenge will consist of the following phases/splits that allow participants to organize their work:

1. Minival phase: The purpose of this phase is sanity checking — to confirm that remote evaluation reports the same result as local evaluation. Each team is allowed up to 100 submissions per day. We will disqualify teams that spam the servers.
2. Test standard phase: The purpose of this phase/split is to serve as the public leaderboard establishing the state of the art. This is what should be used to report results in papers. Each team is allowed up to 10 submissions per day, to be used judiciously.
3. Test challenge phase: This split will be used to decide challenge teams who will proceed to Stage 2 Evaluation. Each team is allowed a total of 5 submissions until the end of challenge submission phase. The highest performing of these 5 will be automatically chosen.

The agents will be evaluated on an AWS GPU-enabled instance. Agents will be evaluated on 1000 episodes and will have a total available time of 48 hours to finish each run. Submissions will be evaluated on AWS EC2 p2.xlarge instance which has a Tesla K80 GPU (12 GB Memory), 4 CPU cores, and 61 GB RAM.

Note: Before pushing the submissions for remote evaluation, participants should test the their submission docker locally to make sure it is working.

Stage 2: Physical Robot 2

A full open-source control stack will be released by Meta AI (the github discussed above is currently in beta), in collaboration with Hello Robot, for the hardware challenge. Once systems have been successfully tested and ranked in simulation, we will evaluate top-3 teams on a physical robot. The final evaluation scenario is a fully held-out apartment with novel objects. All participants’ code will be run with the same default calibrations for fair comparison. We will reset the scene to several predefined configurations. Robots will be run repeatedly on each scene to allow for demonstration of repeatability and statistical significance testing of the results.

HomeRobot OVMM White Paper

We have also posted a more extensive white paper about the OVMM benchmark which includes details of how the environments were constructed, the baselines defined/trained, the hardware setup we used, and a more comprehensive treatment of the related literature: HomeRobot: Open-Vocabulary Mobile Manipulation [arXiv] [low-res 8MB]

Citing Challenge

@inproceedings{homerobotovmmchallenge2023,
  title         =     {The HomeRobot Open Vocab Mobile Manipulation Challenge},
  author        =     {Sriram Yenamandra and Arun Ramachandran and Mukul Khanna and Karmesh Yadav and Devendra Singh Chaplot and Gunjan Chhablani and Alexander Clegg and Theophile Gervet and Vidhi Jain and Ruslan Partsey and Ram Ramrakhya and Andrew Szot and Tsung-Yen Yang and Aaron Edsinger and Charlie Kemp and Binit Shah and Zsolt Kira and Dhruv Batra and Roozbeh Mottaghi and Yonatan Bisk and Chris Paxton},
  booktitle     =     {Thirty-seventh Conference on Neural Information Processing Systems: Competition Track},
  url           =     {https://aihabitat.org/challenge/2023_homerobot_ovmm/},
  year          =     {2023}
}

Citing White Paper

For convenience, we are also including BibTex for the white paper if you’re interested in the benchmark and software stack, rather than the challenge

@misc{homerobotovmm,
  title         =     {HomeRobot: Open Vocab Mobile Manipulation},
  author        =     {Sriram Yenamandra and Arun Ramachandran and Karmesh Yadav and Austin Wang and Mukul Khanna and Theophile Gervet and Tsung-Yen Yang and Vidhi Jain and Alex William Clegg and John Turner and Zsolt Kira and Manolis Savva and Angel Chang and Devendra Singh Chaplot and Dhruv Batra and Roozbeh Mottaghi and Yonatan Bisk and Chris Paxton},
  url           =     {https://aihabitat.org/static/challenge/home_robot_ovmm_2023/OVMM.pdf},
  year          =     {2023}
}

Acknowledgments

The HomeRobot OVMM would not have been possible without the infrastructure and support of Hello Robot 2 and EvalAI 3 team.

References

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^ a b Habitat: A Platform for Embodied AI Research. Manolis Savva*, Abhishek Kadian*, Oleksandr Maksymets*, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, Dhruv Batra. ICCV, 2019.

2.

^ a b c The Design of Stretch: A compact, lightweight mobile manipulator for indoor human environments. Charles C. Kemp, Aaron Edsinger, Henry M. Clever, and Blaine Matulevich. ICRA, 2022.

3.

^ a b Eval AI: Towards Better Evaluation Systems for AI Agents. Deshraj Yadav, Rishabh Jain, Harsh Agrawal, Prithvijit Chattopadhyay, Taranjeet Singh, Akash Jain, Shiv Baran Singh, Stefan Lee, Dhruv Batra. SOSP, 2019.

4.

^ Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols. Berk Calli, Aaron Walsman, Arjun Singh, Siddhartha Srinivasa, Pieter Abbeel, Aaron M. Dollar. Robotics and Automation Magazine (RAM), 2015.

*.

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