# I2EDL: Interactive Instruction Error Detection and Localization

arXiv: https://arxiv.org/abs/2406.05080 · HTML: https://mlsanity.com/paper/2406.05080/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 26 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 22 of 22)

- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2004.02857/) (2020)
- [History Aware Multimodal Transformer for Vision-and-Language Navigation](https://mlsanity.com/paper/2110.13309/) (2021)
- [Vision-and-Dialog Navigation](https://mlsanity.com/paper/1907.04957/) (2019)
- [ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2304.03047/) (2023)
- [Bridging the Gap Between Learning in Discrete and Continuous Environments for Vision-and-Language Navigation](https://mlsanity.com/paper/2203.02764/) (2022)
- [BEVBert: Multimodal Map Pre-training for Language-guided Navigation](https://mlsanity.com/paper/2212.04385/) (2022)
- [Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning](https://mlsanity.com/paper/1909.01871/) (2019)
- [Vision-based Navigation with Language-based Assistance via Imitation Learning with Indirect Intervention](https://mlsanity.com/paper/1812.04155/) (2018)
- [Just Ask:An Interactive Learning Framework for Vision and Language Navigation](https://mlsanity.com/paper/1912.00915/) (2019)
- [Diagnosing the Environment Bias in Vision-and-Language Navigation](https://mlsanity.com/paper/2005.03086/) (2020)
- [Diagnosing Vision-and-Language Navigation: What Really Matters](https://mlsanity.com/paper/2103.16561/) (2021)
- [RMM: A Recursive Mental Model for Dialog Navigation](https://mlsanity.com/paper/2005.00728/) (2020)
- [The RobotSlang Benchmark: Dialog-guided Robot Localization and Navigation](https://mlsanity.com/paper/2010.12639/) (2020)
- [AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments](https://mlsanity.com/paper/2210.07940/) (2022)
- [VISITRON: Visual Semantics-Aligned Interactively Trained Object-Navigator](https://mlsanity.com/paper/2105.11589/) (2021)
- [Mind the Error! Detection and Localization of Instruction Errors in Vision-and-Language Navigation](https://mlsanity.com/paper/2403.10700/) (2024)
- [Language-enhanced RNR-Map: Querying Renderable Neural Radiance Field maps with natural language](https://mlsanity.com/paper/2308.08854/) (2023)
- [Which way is `right'?: Uncovering limitations of Vision-and-Language Navigation model](https://mlsanity.com/paper/2312.00151/) (2023)

## Built on by (most cited first, 1 of 1)

- [Collaborative Instance Object Navigation: Leveraging Uncertainty-Awareness to Minimize Human-Agent Dialogues](https://mlsanity.com/paper/2412.01250/) (2024)

## Abstract (arXiv)

In the Vision-and-Language Navigation in Continuous Environments (VLN-CE) task, the human user guides an autonomous agent to reach a target goal via a series of low-level actions following a textual instruction in natural language. However, most existing methods do not address the likely case where users may make mistakes when providing such instruction (e.g. "turn left" instead of "turn right"). In this work, we address a novel task of Interactive VLN in Continuous Environments (IVLN-CE), which allows the agent to interact with the user during the VLN-CE navigation to verify any doubts regarding the instruction errors. We propose an Interactive Instruction Error Detector and Localizer (I2EDL) that triggers the user-agent interaction upon the detection of instruction errors during the navigation. We leverage a pre-trained module to detect instruction errors and pinpoint them in the instruction by cross-referencing the textual input and past observations. In such way, the agent is able to query the user for a timely correction, without demanding the user's cognitive load, as we locate the probable errors to a precise part of the instruction. We evaluate the proposed I2EDL on a dataset of instructions containing errors, and further devise a novel metric, the Success weighted by Interaction Number (SIN), to reflect both the navigation performance and the interaction effectiveness. We show how the proposed method can ask focused requests for corrections to the user, which in turn increases the navigation success, while minimizing the interactions.