Probable Object Location (POLo) Score Estimation for Efficient Object Goal Navigation

Probable Object Location (POLo) Score Estimation for Efficient Object Goal Navigation, Jiaming Wang★, Harold Soh★, IEEE International Conference on Robotics and Automation
Links: Paper | Github

To advance the field of autonomous robotics, particularly in object search tasks within unexplored environments, we introduce a novel framework centered around the Probable Object Location (POLo) score. Utilizing a 3D object probability map, the POLo score allows the agent to make data-driven decisions for efficient object search. We further enhance the framework’s practicality by introducing POLoNet, a neural network trained to approximate the computationally intensive POLo score. Our approach addresses critical limitations of both end-to-end reinforcement learning methods, which suffer from memory decay over long-horizon tasks, and traditional map-based methods that neglect visibility constraints. Our experiments, involving the first phase of the OVMM 2023 challenge, demonstrate that an agent equipped with POLoNet significantly outperforms a range of baseline methods, including end-to-end RL techniques and prior map-based strategies. To provide a comprehensive evaluation, we introduce new performance metrics that offer insights into the efficiency and effectiveness of various agents in object goal navigation.

Resources

You can find our paper here. Check out our repository here on github

Citation

Please consider citing our paper if you build upon our results and ideas.

Jiaming Wang★, Harold Soh★, “Probable Object Location (POLo) Score Estimation for Efficient Object Goal Navigation”, IEEE International Conference on Robotics and Automation

@article{wang2023probable, title={Probable Object Location (POLo) Score Estimation for Efficient Object Goal Navigation}, author={Wang, Jiaming and Soh, Harold}, journal={arXiv preprint arXiv:2311.07992}, year={2023}}

Contact

If you have questions or comments, please contact Jiaming or Harold.

Acknowledgements

This research is supported by the National Research Foundation, Singapore under its Medium Sized Center for Advanced Robotics Technology Innovation.