DOZE: A Dataset for Open-Vocabulary Zero-Shot Object Navigation in Dynamic Environments

מושגי ליבה

The author proposes DOZE, a dataset addressing the limitations of existing datasets for Zero-Shot Object Navigation by incorporating dynamic obstacles, open-vocabulary objects, distinct-attribute objects, and hint objects. The dataset aims to challenge ZSON methods and improve navigation efficiency, safety, and object recognition accuracy.

תקציר

DOZE introduces a novel dataset for Zero-Shot Object Navigation in dynamic environments. It includes static and dynamic humanoid obstacles, diverse objects with unique attributes, and textual hints for enhanced contextual understanding. The dataset aims to test ZSON methods' capabilities and improve their performance across various challenges.
Key points:

DOZE addresses limitations of existing ZSON datasets by including dynamic obstacles and diverse object attributes.
The dataset features open-vocabulary objects, distinct-attribute objects, and hint objects to enhance agent navigation.
Evaluation of ZSON methods on DOZE reveals room for improvement in navigation efficiency and object recognition accuracy.

סטטיסטיקה

"DOZE comprises ten high-fidelity 3D scenes with over 18k tasks."
"Four representative ZSON methods were tested on DOZE."
"C-L3MVN achieved a success rate of over 32% on Level 1 tasks."

ציטוטים

"No moving objects are incorporated into the associated ObjectNav tasks."
"Existing datasets often contain no textual hints for object localization."
"Our dataset is the first ObjectNav dataset that integrates moving objects in the scene."

תובנות מפתח מזוקקות מ:

DOZE

by Ji Ma,Hongmi... ב- arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.19007.pdf

שאלות מעמיקות

How can the incorporation of dynamic obstacles enhance agent navigation abilities beyond traditional scenarios

Incorporating dynamic obstacles in agent navigation scenarios goes beyond traditional static environments by introducing a layer of unpredictability and real-world complexity. Dynamic obstacles, such as moving humanoid figures, add a level of challenge that requires agents to adapt their navigation strategies in real-time. This enhancement forces agents to consider not only the current state of the environment but also anticipate future movements and adjust their paths accordingly. By navigating around dynamic obstacles, agents improve their collision avoidance skills, spatial reasoning abilities, and overall adaptability in changing environments. This dynamic element mirrors real-world scenarios where objects are not stationary but move unpredictably, preparing agents for more practical applications where they must navigate through crowded or constantly changing spaces.

What are the implications of introducing hint information in ZSON tasks for real-world applications

The introduction of hint information in Zero-Shot Object Navigation (ZSON) tasks has significant implications for real-world applications by enhancing the efficiency and accuracy of goal object localization. In practical settings, environmental cues like textual hints play a crucial role in aiding navigation and decision-making processes. By incorporating hint information into ZSON tasks, agents can leverage contextual clues to deduce the potential location of goal objects more effectively. This integration enables agents to prioritize search areas based on textual guidance, leading to quicker identification and retrieval of target objects within complex environments. The utilization of hints not only improves navigation performance but also reflects how AI systems can benefit from multimodal inputs for enhanced understanding and decision-making capabilities.

How might the lack of open-vocabulary objects impact the generalization ability of perception models

The absence of open-vocabulary objects in training datasets can significantly impact the generalization ability of perception models in Zero-Shot Object Navigation (ZSON) tasks. Open-vocabulary objects refer to items that may not be explicitly defined or categorized during training but are encountered during actual deployment or testing phases. Without exposure to a diverse range of open-vocabulary objects during training, perception models may struggle when faced with unfamiliar or untrained object categories during inference. This limitation hinders the model's capacity to recognize and localize novel objects outside its training data distribution accurately.
To address this challenge effectively, it is essential to incorporate open-vocabulary objects into ZSON datasets during training sessions so that perception models can learn robust feature representations capable of generalizing across various object categories seamlessly.