RING (Robotic Indoor Navigation Generalist) is a generalist policy for indoor visual navigation trained solely in simulation with diverse, randomly initialized embodiments at scale (1M embodiments). Despite training exclusively in simulation, RING demonstrates robust performance on unseen real-world robots (RB‑Y1, Stretch RE‑1, LoCoBot, Unitree Go1).
Maps egocentric visual observations and a natural-language goal to a discrete navigation action.
Trained for robust generalization across embodiments and environments.
Intended use
Research in embodied AI, navigation policy learning, sim2real transfer, and generalist robot policies.
Evaluation and benchmarking for indoor visual navigation tasks.
Checkpoints
Default checkpoint: ring_model_step_40356421.ckpt
Quick Start
Python (download checkpoint)
from huggingface_hub import hf_hub_download
ckpt_path = hf_hub_download(
repo_id="AinazEftekhar/OneRING",
filename="ring_model_step_40356421.ckpt"
)
print(ckpt_path) # local path to the checkpoint file
