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Bridging the Domain Gap in Instance-specific Image Goal Navigation for Service Robots using Contrastive Learning

Core Concepts
Leveraging contrastive learning and image enhancement techniques to effectively bridge the domain gap between low-quality robot observations and high-quality query images, enabling robust instance-specific object localization.
The proposed system addresses the challenge of instance-specific image goal navigation (InstanceImageNav) for service robots, where the task is to locate an object identical to a query image provided by the user within the robot's observed environment. The key challenge lies in the significant domain gap between the low-quality images captured by the moving robot, characterized by motion blur and low resolution, and the high-quality query images provided by the user. To mitigate this issue, the system integrates two key mechanisms: Learning domain-invariant feature representations between low-quality robot observations and few high-quality user-provided images through contrastive learning with an instance classifier (CrossIA). This effectively aligns the latent representations of cross-quality images on an instance basis. Enhancing the quality of observed images by integrating a pre-trained deblurring model into the object image database construction process. The proposed system first constructs a 3D semantic map and collects object images during the robot's exploration of the environment. It then fine-tunes a pre-trained SimSiam model using the CrossIA approach, leveraging the collected low-quality images and a few high-quality user-provided images. Experiments on a dataset of 20 different object instances show that the proposed system significantly outperforms the baseline methods, improving the task success rate by up to three times. The results highlight the effectiveness of combining contrastive learning and image enhancement techniques to bridge the domain gap and enable robust instance-level object localization for service robots.
The robot collected 606 RGBD images covering a 74 m^2 area in 2 minutes, resulting in a dataset of 2011 images across 145 instances. The query images were captured using an iPhone 11 Pro from a distance of 40 cm, with 8 images taken for each instance from different angles.
"Improving instance-specific image goal navigation (InstanceImageNav), which locates the identical object in a real-world environment from a query image, is essential for robotic systems to assist users in finding desired objects." "The challenge lies in the domain gap between low-quality images observed by the moving robot, characterized by motion blur and low-resolution, and high-quality query images provided by the user."

Deeper Inquiries

How could the proposed system be extended to handle a larger number of instances or a more diverse set of object categories

To handle a larger number of instances or a more diverse set of object categories, the proposed system could be extended in several ways. One approach could involve implementing a more robust data collection module that can autonomously gather object images and semantic information from a wider range of environments. This could include incorporating advanced perception capabilities to identify and categorize new instances efficiently. Additionally, the system could leverage transfer learning techniques to adapt to new instances or object categories by fine-tuning the pre-trained models with a smaller amount of new data. By enhancing the scalability and adaptability of the data collection and fine-tuning processes, the system can effectively handle a larger number of instances and a more diverse set of object categories.

What other techniques, beyond contrastive learning and image enhancement, could be explored to further bridge the domain gap in InstanceImageNav

Beyond contrastive learning and image enhancement, several other techniques could be explored to further bridge the domain gap in InstanceImageNav. One potential approach is to incorporate domain adaptation methods that focus on aligning the feature distributions between different domains. Adversarial domain adaptation, for instance, could be utilized to learn domain-invariant representations by minimizing the distribution discrepancy between low-quality robot-observed images and high-quality user-provided images. Additionally, self-supervised learning techniques, such as generative modeling or meta-learning, could be employed to learn robust representations that generalize well across different image qualities and domains. By combining these techniques with contrastive learning and image enhancement, the system can achieve even greater domain adaptation and feature alignment, improving object localization performance in diverse real-world environments.

How might the user interaction and feedback be incorporated to iteratively improve the system's performance over time

Incorporating user interaction and feedback into the system can play a crucial role in iteratively improving its performance over time. One way to integrate user feedback is to implement a feedback loop mechanism where users can provide corrections or additional information about the identified objects. This feedback can be used to update the system's object representations and improve its accuracy in future instances. Furthermore, active learning strategies can be employed to selectively request user input for instances where the system is uncertain, effectively leveraging user interaction to enhance the system's learning process. Additionally, implementing a user-friendly interface that allows users to easily provide feedback and annotations can facilitate continuous improvement and adaptation of the system based on real-world user interactions. By incorporating user feedback mechanisms, the system can continuously learn and refine its object localization capabilities, leading to enhanced performance and user satisfaction.