The One RING: a Robotic Indoor Navigation Generalist
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2024
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| author | Eftekhar, Ainaz Hendrix, Rose Weihs, Luca Duan, Jiafei Caglar, Ege Salvador, Jordi Herrasti, Alvaro Han, Winson VanderBil, Eli Kembhavi, Aniruddha Farhadi, Ali Krishna, Ranjay Ehsani, Kiana Zeng, Kuo-Hao |
| author_facet | Eftekhar, Ainaz Hendrix, Rose Weihs, Luca Duan, Jiafei Caglar, Ege Salvador, Jordi Herrasti, Alvaro Han, Winson VanderBil, Eli Kembhavi, Aniruddha Farhadi, Ali Krishna, Ranjay Ehsani, Kiana Zeng, Kuo-Hao |
| contents | Modern robots vary significantly in shape, size, and sensor configurations used to perceive and interact with their environments. However, most navigation policies are embodiment-specific--a policy trained on one robot typically fails to generalize to another, even with minor changes in body size or camera viewpoint. As custom hardware becomes increasingly common, there is a growing need for a single policy that generalizes across embodiments, eliminating the need to retrain for each specific robot. In this paper, we introduce RING (Robotic Indoor Navigation Generalist), an embodiment-agnostic policy that turns any mobile robot into an effective indoor semantic navigator. Trained entirely in simulation, RING leverages large-scale randomization over robot embodiments to enable robust generalization to many real-world platforms. To support this, we augment the AI2-THOR simulator to instantiate robots with controllable configurations, varying in body size, rotation pivot point, and camera parameters. On the visual object-goal navigation task, RING achieves strong cross-embodiment (XE) generalization--72.1% average success rate across five simulated embodiments (a 16.7% absolute improvement on the Chores-S benchmark) and 78.9% across four real-world platforms, including Stretch RE-1, LoCoBot, and Unitree Go1--matching or even surpassing embodiment-specific policies. We further deploy RING on the RB-Y1 wheeled humanoid in a real-world kitchen environment, showcasing its out-of-the-box potential for mobile manipulation platforms. (Project website: https://one-ring-policy.allen.ai) |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14401 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The One RING: a Robotic Indoor Navigation Generalist Eftekhar, Ainaz Hendrix, Rose Weihs, Luca Duan, Jiafei Caglar, Ege Salvador, Jordi Herrasti, Alvaro Han, Winson VanderBil, Eli Kembhavi, Aniruddha Farhadi, Ali Krishna, Ranjay Ehsani, Kiana Zeng, Kuo-Hao Robotics Computer Vision and Pattern Recognition Modern robots vary significantly in shape, size, and sensor configurations used to perceive and interact with their environments. However, most navigation policies are embodiment-specific--a policy trained on one robot typically fails to generalize to another, even with minor changes in body size or camera viewpoint. As custom hardware becomes increasingly common, there is a growing need for a single policy that generalizes across embodiments, eliminating the need to retrain for each specific robot. In this paper, we introduce RING (Robotic Indoor Navigation Generalist), an embodiment-agnostic policy that turns any mobile robot into an effective indoor semantic navigator. Trained entirely in simulation, RING leverages large-scale randomization over robot embodiments to enable robust generalization to many real-world platforms. To support this, we augment the AI2-THOR simulator to instantiate robots with controllable configurations, varying in body size, rotation pivot point, and camera parameters. On the visual object-goal navigation task, RING achieves strong cross-embodiment (XE) generalization--72.1% average success rate across five simulated embodiments (a 16.7% absolute improvement on the Chores-S benchmark) and 78.9% across four real-world platforms, including Stretch RE-1, LoCoBot, and Unitree Go1--matching or even surpassing embodiment-specific policies. We further deploy RING on the RB-Y1 wheeled humanoid in a real-world kitchen environment, showcasing its out-of-the-box potential for mobile manipulation platforms. (Project website: https://one-ring-policy.allen.ai) |
| title | The One RING: a Robotic Indoor Navigation Generalist |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.14401 |