Ben Huh, Avinash Baidya
NeurIPS 2022
We address a practical yet challenging problem of training robot agents to navigate in an environment following a path described by some language instructions. The instructions often contain descriptions of objects in the environment. To achieve accurate and efficient navigation, it is critical to build a map that accurately represents both spatial location and the semantic information of the environment objects. However, enabling a robot to build a map that well represents the environment is extremely challenging as the environment often involves diverse objects with various attributes. In this paper, we propose a multi-granularity map, which contains both object fine-grained details (e.g., color, texture) and semantic classes, to represent objects more comprehensively. Moreover, we propose a weakly-supervised auxiliary task, which requires the agent to localize instruction-relevant objects on the map. Through this task, the agent not only learns to localize the instruction-relevant objects for navigation but also is encouraged to learn a better map representation that reveals object information. We then feed the learned map and instruction to a waypoint predictor to determine the next navigation goal. Experimental results show our method outperforms the state-of-the-art by 4.0% and 4.6% w.r.t. success rate both in seen and unseen environments, respectively on VLN-CE dataset. Code is available at https://github.com/PeihaoChen/WS-MGMap.
Ben Huh, Avinash Baidya
NeurIPS 2022
Hongyu Tu, Shantam Shorewala, et al.
NeurIPS 2022
Chanakya Ekbote, Moksh Jain, et al.
NeurIPS 2022
Shiqiang Wang, Nathalie Baracaldo Angel, et al.
NeurIPS 2022