ASSISTER: Assistive Navigation via Conditional Instruction Generation

Zanming Huang, Zhongkai Shangguan, Jimuyang Zhang, Gilad Bar, Matthew Boyd, Eshed Ohn-Bar ;

"We introduce a novel vision-and-language navigation (VLN) task of learning to provide real-time guidance to a blind follower situated in complex dynamic navigation scenarios. Towards exploring real-time information needs and fundamental challenges in our novel modeling task, we first collect a multi-modal real-world benchmark with in-situ Orientation and Mobility (O&M) instructional guidance. Subsequently, we leverage the real-world study to inform the design of a larger-scale simulation benchmark, thus enabling comprehensive analysis of limitations in current VLN models. Motivated by how sighted O&M guides seamlessly and safely support the awareness of individuals with visual impairments when collaborating on navigation tasks, we present ASSISTER, an imitation-learned agent that can embody such effective guidance. The proposed assistive VLN agent is conditioned on navigational goals and commands for generating instructional sentences that are coherent with the surrounding visual scene, while also carefully accounting for the immediate assistive navigation task. Altogether, our introduced evaluation and training framework takes a step towards scalable development of the next generation of seamless, human-like assistive agents."