REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments (1904.10151v2)

Abstract: One of the long-term challenges of robotics is to enable robots to interact with humans in the visual world via natural language, as humans are visual animals that communicate through language. Overcoming this challenge requires the ability to perform a wide variety of complex tasks in response to multifarious instructions from humans. In the hope that it might drive progress towards more flexible and powerful human interactions with robots, we propose a dataset of varied and complex robot tasks, described in natural language, in terms of objects visible in a large set of real images. Given an instruction, success requires navigating through a previously-unseen environment to identify an object. This represents a practical challenge, but one that closely reflects one of the core visual problems in robotics. Several state-of-the-art vision-and-language navigation, and referring-expression models are tested to verify the difficulty of this new task, but none of them show promising results because there are many fundamental differences between our task and previous ones. A novel Interactive Navigator-Pointer model is also proposed that provides a strong baseline on the task. The proposed model especially achieves the best performance on the unseen test split, but still leaves substantial room for improvement compared to the human performance.

• The paper introduces the REVERIE challenge, advancing embodied AI by combining language understanding with object-level visual navigation in complex indoor environments.
• The paper details a comprehensive dataset paired with an enhanced simulator to rigorously support navigation tasks with real-world imagery and succinct language commands.
• The paper evaluates state-of-the-art methods, revealing a significant performance gap and establishing the Interactive Navigator-Pointer model as a strong baseline for future research.

Essay: Analysis of the REVERIE Task and Dataset

The paper introduces a novel challenge in the domain of embodied AI with the REVERIE task—Remote Embodied Visual Referring Expression in Real Indoor Environments. REVERIE synthesizes tasks necessitating complex interactions between language processing and visual navigation within 3D environments. This task is a pointed effort to bridge a significant gap in robotic applications: enabling agents, akin to human children, to perform complex, language-guided visual tasks in unfamiliar environments. The paper's principal contributions are the introduction of a comprehensive dataset specifically crafted for this challenge, enhancement of an existing simulator to support complex navigation tasks with object-level details, and an Interactive Navigator-Pointer (INP) model to baseline the task.

Overview of the Proposed Dataset and Task

The REVERIE dataset represents a significant effort, amalgamating real-world images and natural language commands to push forward research in human-robot interaction. The dataset is unique for its real-world applicability due to the nature of its instructions, which are high-level and succinct, embodying genuine human commands to robots in domestic scenarios. Furthermore, the dataset is supplemented by a sophisticated simulator, Matterport3D, now enhanced with object annotations for rigorous evaluation of visual grounding capabilities.

The design of the REVERIE task itself distinguishes it from existing embodied navigation challenges. Unlike Vision-and-Language Navigation (VLN) tasks, which hinge on location identification, REVERIE prioritizes the identification and grounding of target objects through navigation. This instills a complexity level that mimics more closely the intricacies of real-life robot tasking, where object identification is multifaceted, involving environmental dynamics and linguistic subtleties. Importantly, this task requires navigation through unseen environments, demanding agents to leverage both transferred experience and real-time sensory data for successful task execution.

Performance Evaluation and Analysis

The paper provides a rigorous analysis of existing methods in this new context, highlighting the limitations of current state-of-the-art approaches when faced with the intricate requirements of the REVERIE task. By employing several state-of-the-art navigation algorithms and adapting them under the purview of referencing expression comprehension, it becomes evident that these methods substantially lag behind human performance—a gap highlighted by empirical evaluation. The paper’s exploration of this performance gap underscores the inadequacies in both the understanding and execution of complex, multi-step linguistic and visual tasks by current AI systems.

The Interactive Navigator-Pointer model introduced acts as both a solution proposal and a strong baseline for future research. The INP model strategically allows the navigator and pointer modules to share and utilize insights reciprocally, thereby demonstrating improved performance over non-integrative attempts. This model, through effectively combining visual grounding insights with navigational deducements, exemplifies a critical step towards more intelligent agents capable of true embodiment in task execution.

Implications and Future Directions

The implications of the REVERIE task are multifaceted. Practically, the task hints at the pressing necessity for better integration of sensory inputs and task strategizing in robotics, particularly within human-centric environments. Theoretically, the challenge encourages revisiting and refining interdisciplinary approaches that combine AI, linguistics, and robotics.

As the field advances, future developments might involve enhancing the embodied AI’s perceptive capabilities through more robust, multi-sensory integrations and advancing models that adeptly navigate abstract and concrete layers of textual instructions. Moreover, improvements in contextual learning and transfer learning—leveraging knowledge from known domains to unprecedented scenarios—will be crucial.

In summary, the REVERIE task and dataset act as a pivotal milestone in pushing the boundaries of what current embodied AI systems can achieve. By setting a standard for future AI advancements in interactive, language-guided robotics, the paper successfully lays the groundwork for innovations that align closely with real-world human-robot interaction expectations.