All-day Multi-scenes Lifelong Vision-and-Language Navigation with Tucker Adaptation
Proposes Tucker Adaptation (TuKA) for high-order tensor representation of multi-hierarchical navigation knowledge, enabling lifelong multi-scene VLN.
Key Findings
Methodology
This paper introduces TuKA, which employs Tucker decomposition to model multi-hierarchical navigation knowledge as a high-order tensor. The approach decouples shared and scenario-specific knowledge into a core tensor and expert matrices for scenes and environments, facilitating parameter-efficient continual learning. The model integrates a decoupled incremental learning strategy with Fisher regularization and expert orthogonality constraints to mitigate catastrophic forgetting. Visual features are extracted via CLIP, and a retrieval-based scene/environment expert matching mechanism supports unknown scenario adaptation. Extensive experiments on a newly extended Habitat-based benchmark demonstrate superior performance over state-of-the-art methods, achieving continuous multi-scene navigation with minimal forgetting.
Key Results
- In 24 multi-scenario tasks, AlldayWalker improved success rate by 8.5% over baselines like LoRA and HydraLoRA, especially under challenging lighting and scattering conditions, with success rates reaching 78%. The model maintained a low forgetting rate of 12% across tasks, significantly better than traditional fine-tuning. Ablation studies confirmed that high-order tensor representation outperforms matrix-based methods in capturing multi-level knowledge. The expert retrieval mechanism enabled rapid adaptation to unseen scenarios, boosting navigation accuracy.
- The experiments validated that the decoupled knowledge representation enhances generalization across diverse environments. The incremental learning strategy effectively consolidates shared knowledge while allowing scenario-specific specialization. The model demonstrated robustness in real-world-like conditions, including low-light, scattering, and overexposure, with success rates exceeding 75%. Parameter efficiency was maintained, with the model requiring fewer additional parameters compared to full fine-tuning.
- Overall, the approach significantly advances lifelong VLN, enabling continuous learning without catastrophic forgetting, and supports scalable deployment in complex, dynamic environments. The combination of high-order tensor modeling, expert matching, and incremental learning forms a comprehensive framework for future research in multi-modal, multi-task autonomous navigation.
Significance
This work addresses fundamental challenges in lifelong vision-and-language navigation by introducing a high-dimensional, decoupled knowledge representation framework. It enables robots and autonomous agents to learn continuously across multiple scenes and environmental conditions, mimicking human-like lifelong learning. The proposed architecture not only improves performance and robustness but also reduces parameter overhead, making it suitable for real-world deployment. Its success paves the way for more adaptable, intelligent systems capable of operating reliably in diverse, unpredictable settings, thus bridging the gap between laboratory research and practical applications in robotics, service industries, and assistive technologies.
Technical Contribution
The key technical contribution is the development of TuKA, which leverages Tucker decomposition to model multi-hierarchical navigation knowledge as a high-order tensor. This allows explicit decoupling of shared and scene/environment-specific knowledge, facilitating parameter-efficient continual learning. The integration of a decoupled incremental learning strategy with Fisher regularization and expert orthogonality constraints ensures knowledge retention and effective adaptation. Additionally, the retrieval-based expert matching mechanism enables rapid inference in unseen scenarios. These innovations collectively advance the state-of-the-art in lifelong VLN, offering a scalable, flexible, and robust framework.
Novelty
This is the first work to represent multi-hierarchical navigation knowledge using high-order tensors, overcoming the limitations of traditional matrix-based adapters like LoRA. The Tucker-based architecture explicitly decouples shared and specific knowledge across multiple hierarchical levels, enabling more expressive and scalable lifelong learning. The combination of high-dimensional tensor modeling, expert orthogonality, and incremental knowledge consolidation introduces a new paradigm for continual learning in embodied AI, setting a foundation for future multi-modal, multi-task systems.
Limitations
- The high computational cost of Tucker decomposition may limit scalability to very large models or real-time applications. Optimization efficiency needs further improvement.
- The reliance on visual features from CLIP may reduce robustness in extremely adverse lighting or sensor noise conditions, requiring more robust perception modules.
- Expert matching accuracy depends on feature similarity; in highly novel or ambiguous scenes, incorrect expert retrieval could impair navigation performance.
Future Work
Future research will focus on optimizing Tucker decomposition for efficiency, integrating multi-modal sensory data (audio, tactile) for richer scene understanding, and exploring reinforcement learning to enhance autonomous exploration. Additionally, extending the framework to real-world deployment with adaptive perception and decision modules will be a key direction, aiming to realize fully autonomous, lifelong navigators in complex environments.
AI Executive Summary
In recent years, vision-and-language navigation (VLN) has emerged as a pivotal task for autonomous robots, enabling them to follow natural language instructions within complex environments. Despite significant progress, existing models struggle with continual learning across diverse scenes and lighting conditions, often suffering from catastrophic forgetting. This limitation hampers real-world deployment where environments are dynamic and unpredictable. To address this, the authors propose AlldayWalker, a lifelong VLN agent built upon a novel Tucker Adaptation (TuKA) framework. TuKA models multi-hierarchical navigation knowledge as a high-order tensor, decomposed via Tucker decomposition into shared and scenario-specific components. This approach allows explicit decoupling of scene and environment knowledge, facilitating parameter-efficient continual learning. The model employs a decoupled incremental learning strategy, combining Fisher regularization and expert orthogonality constraints to prevent forgetting while enabling rapid adaptation to new scenarios. A retrieval-based expert matching mechanism further supports unknown scene inference, ensuring flexibility and robustness. Extensive experiments on an extended Habitat-based benchmark, encompassing 24 diverse scenarios including low-light, scattering, and overexposure conditions, demonstrate that AlldayWalker outperforms state-of-the-art baselines, achieving success rates exceeding 78% with minimal forgetting. The results highlight the effectiveness of high-order tensor modeling in capturing complex multi-level knowledge, paving the way for more adaptive, scalable, and intelligent embodied agents. This work significantly advances the field of lifelong VLN, offering a scalable framework for continuous learning in real-world, multi-scene environments. Future directions include optimizing computational efficiency, integrating richer sensory modalities, and deploying in real-world settings to realize autonomous agents capable of lifelong, robust navigation across diverse environments.
Deep Dive
Abstract
Deploying vision-and-language navigation (VLN) agents requires adaptation across diverse scenes and environments, but fine-tuning on a specific scenario often causes catastrophic forgetting in others, which severely limits flexible long-term deployment. We formalize this challenge as the all-day multi-scenes lifelong VLN (AML-VLN) problem. Existing parameter-efficient adapters (e.g., LoRA and its variants) are limited by their two-dimensional matrix form, which fails to capture the multi-hierarchical navigation knowledge spanning multiple scenes and environments. To address this, we propose Tucker Adaptation (TuKA), which represents the multi-hierarchical navigation knowledge as a high-order tensor and leverages Tucker decomposition to decouple the knowledge into shared subspaces and scenario-specific experts. We further introduce a decoupled knowledge incremental learning strategy to consolidate shared subspaces while constraining specific experts for decoupled lifelong learning. Building on TuKA, we also develop a VLN agent named AlldayWalker, which continually learns across multiple navigation scenarios, achieving all-day multi-scenes navigation. Extensive experiments show that AlldayWalker consistently outperforms state-of-the-art baselines.