Mind the Error! Detection and Localization of Instruction Errors in Vision-and-Language Navigation
This addresses the fragility of VLN-CE systems to human errors in instructions, which is a domain-specific problem for embodied AI, but the work is incremental as it builds on existing VLN-CE tasks.
The paper tackles the problem of erroneous human instructions in Vision-and-Language Navigation in Continuous Environments (VLN-CE) by introducing a novel benchmark dataset with various error types, observing a performance drop of up to -25% in Success Rate for state-of-the-art methods, and proposing a cross-modal transformer method that achieves the best performance in error detection and localization.
Vision-and-Language Navigation in Continuous Environments (VLN-CE) is one of the most intuitive yet challenging embodied AI tasks. Agents are tasked to navigate towards a target goal by executing a set of low-level actions, following a series of natural language instructions. All VLN-CE methods in the literature assume that language instructions are exact. However, in practice, instructions given by humans can contain errors when describing a spatial environment due to inaccurate memory or confusion. Current VLN-CE benchmarks do not address this scenario, making the state-of-the-art methods in VLN-CE fragile in the presence of erroneous instructions from human users. For the first time, we propose a novel benchmark dataset that introduces various types of instruction errors considering potential human causes. This benchmark provides valuable insight into the robustness of VLN systems in continuous environments. We observe a noticeable performance drop (up to -25%) in Success Rate when evaluating the state-of-the-art VLN-CE methods on our benchmark. Moreover, we formally define the task of Instruction Error Detection and Localization, and establish an evaluation protocol on top of our benchmark dataset. We also propose an effective method, based on a cross-modal transformer architecture, that achieves the best performance in error detection and localization, compared to baselines. Surprisingly, our proposed method has revealed errors in the validation set of the two commonly used datasets for VLN-CE, i.e., R2R-CE and RxR-CE, demonstrating the utility of our technique in other tasks. Code and dataset available at https://intelligolabs.github.io/R2RIE-CE