Linear Attention Policy Outperforms Transformers for Open-Vocabulary Object Goal Navigation
A new policy architecture called LANav, based on linear attention, has been proposed for open-vocabulary object goal navigation. LANav achieves a 36.4% success rate on the HM3D-OVON benchmark, outperforming Transformer-based baselines by 6.3 percentage points. The method also demonstrates an 82% success rate in real-world tests on a Unitree Go2 robot, indicating strong sim-to-real transfer and practical feasibility.
Why it matters: This work demonstrates that structured linear attention mechanisms can surpass Transformer architectures for navigation tasks, potentially leading to more efficient and effective robotic navigation systems.
Full story at: arXiv Robotics ↗