HomeRobot: Open-Vocabulary Mobile Manipulation
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Abstract (arXiv)
HomeRobot (noun): An affordable compliant robot that navigates homes and manipulates a wide range of objects in order to complete everyday tasks. Open-Vocabulary Mobile Manipulation (OVMM) is the problem of picking any object in any unseen environment, and placing it in a commanded location. This is a foundational challenge for robots to be useful assistants in human environments, because it involves tackling sub-problems from across robotics: perception, language understanding, navigation, and manipulation are all essential to OVMM. In addition, integration of the solutions to these sub-problems poses its own substantial challenges. To drive research in this area, we introduce the HomeRobot OVMM benchmark, where an agent navigates household environments to grasp novel objects and place them on target receptacles. HomeRobot has two components: a simulation component, which uses a large and diverse curated object set in new, high-quality multi-room home environments; and a real-world component, providing a software stack for the low-cost Hello Robot Stretch to encourage replication of real-world experiments across labs. We implement both reinforcement learning and heuristic (model-based) baselines and show evidence of sim-to-real transfer. Our baselines achieve a 20% success rate in the real world; our experiments identify ways future research work improve performance. See videos on our website: https://ovmm.github.io/.
Key facts. Cited by 104 papers in the map (data through 7 Oct 2026). Builds on 76 of its 116 references that are in the map. First posted on arXiv 20 Jun 2023.
Builds on
76 of 116 references.
References · 76
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- 2019Interactive Gibson Benchmark (iGibson 0.5): A Benchmark for Interactive Navigation in Cluttered Environmentscited by 89
- 2018Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitationcited by 76
- 2018Robot Learning in Homes: Improving Generalization and Reducing Dataset Biascited by 75
50 most cited of 76 All 76 (JSON)
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Citing papers per year: 2023: 7; 2024: 38; 2025: 33; 2026: 26.
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