SAME: Learning Generic Language-Guided Visual Navigation with State-Adaptive Mixture of Experts
SAME employs a state-adaptive Mixture of Experts for multi-task visual navigation, outperforming task-specific models with dynamic expert routing.
Key Findings
Methodology
This paper introduces a multi-task navigation framework leveraging a novel State-Adaptive Mixture of Experts (SAME). The model comprises multiple specialized expert networks, each trained for specific navigation skills such as exploration or instruction-following. A routing mechanism dynamically selects experts based on the agent's current state, which includes visual observations and language features, encoded via cross-modal transformers. The experts are integrated within a sparse MoE layer applied to visual queries, facilitating multimodal feature fusion. The training involves joint optimization across seven datasets (R2R, REVERIE, OBJECTNAV, etc.), utilizing pre-trained vision-language models like CLIP for feature extraction. This design enables the model to adaptively switch between skills, effectively sharing knowledge and mitigating task conflicts, thus achieving high performance across diverse navigation tasks.
Key Results
- The proposed model achieved state-of-the-art or comparable results on seven benchmarks, with success rates reaching 74.5% on R2R, 45.67% on REVERIE, and 73.39% on OBJECTNAV. Multi-task training improved generalization, with an average SPL of 48.31%. Ablation studies confirmed that the state-adaptive routing mechanism outperforms task-level or token-level MoE, especially when applied to visual queries. Pre-training with ScaleVLN further boosted performance, demonstrating the benefit of large-scale vision-language pretraining. The model effectively balances exploration and instruction following, adapting dynamically to environmental changes and instruction granularity.
- Experiment results show that the expert routing based on multimodal features significantly enhances the agent’s ability to handle diverse tasks. The visual query experts enable precise attention over multiple viewpoints, improving action decision accuracy. The model’s robustness across environments and instruction complexities indicates strong generalization. Comparisons with baselines reveal a 3-5% success rate increase, validating the effectiveness of the proposed approach. The experiments also highlight the importance of expert position and routing strategy choices, with the state-adaptive mechanism providing the best overall performance.
- These findings demonstrate that dynamic expert selection based on multimodal cues allows the agent to learn transferable skills, effectively managing the exploration-exploitation trade-off. The integration of pre-trained models and multi-task training strategies ensures scalable and efficient learning, paving the way for more autonomous and versatile robotic systems capable of understanding complex natural language commands in real-world settings.
Significance
This research addresses the long-standing challenge of creating versatile, generalizable navigation agents capable of understanding and executing arbitrary natural language instructions across diverse environments. By introducing the state-adaptive expert routing mechanism, the study significantly advances multi-task learning in embodied AI, enabling knowledge sharing and skill transfer. The approach reduces the need for task-specific models, lowering deployment costs and improving scalability. Its success demonstrates the potential for robots and virtual assistants to operate seamlessly in complex, dynamic settings, bringing closer the vision of autonomous agents capable of natural language interaction and adaptive behavior. This work lays a foundation for future integration with reinforcement learning and real-world robotics, promising broader impact in industry and research.
Technical Contribution
The core technical innovation lies in the design of a state-adaptive expert routing mechanism within a sparse MoE framework, tailored for sequential decision-making tasks. Unlike prior task-level or token-level MoE, this approach dynamically conditions expert selection on multimodal features derived from visual observations and language instructions. The experts are applied at the visual query level within cross-attention layers, enabling fine-grained control over multimodal feature fusion. The model leverages pre-trained vision-language encoders (e.g., CLIP) and large-scale multi-task pretraining (ScaleVLN), facilitating transferability and robustness. The training incorporates a multi-task optimization strategy with a load balancing loss, ensuring effective skill sharing. This design enhances the agent’s ability to adapt to environmental variations and instruction granularity, setting a new standard for multi-task embodied AI.
Novelty
This work is the first to propose a state-adaptive expert routing mechanism specifically designed for sequential embodied navigation tasks. Unlike existing MoE approaches that operate at token or task levels, this method dynamically adapts expert selection based on multimodal features at each timestep, enabling flexible skill management. The integration of visual query experts within a cross-attention framework and the use of pre-trained vision-language models for feature extraction are novel contributions. These innovations collectively address the challenge of multi-task conflicts and generalization, marking a significant departure from conventional task-specific or static MoE models in embodied AI.
Limitations
- Despite its strengths, the model's performance can degrade in highly ambiguous or unseen environments where visual cues are insufficient. The reliance on large-scale pretraining and multiple experts increases computational complexity and training time. The expert routing mechanism, while flexible, may still encounter overfitting to dominant tasks, limiting transferability in highly diverse scenarios. Future work should focus on optimizing routing efficiency, reducing computational costs, and enhancing robustness in real-world applications.
Future Work
Future directions include integrating reinforcement learning to enable autonomous exploration and decision-making, further improving adaptability. Developing more efficient routing algorithms can reduce computational overhead. Extending the framework to incorporate additional modalities like audio or tactile feedback could enrich environmental understanding. Applying this approach to real-world robotic platforms will test its robustness and practicality, paving the way for intelligent agents capable of complex, natural language-guided interactions in dynamic environments.
AI Executive Summary
Deep Dive
Plain Language Accessible to non-experts
想象你在一个大厨房里做饭，里面有很多不同的厨师，每个厨师都擅长某一项任务，比如切菜、炒菜、摆盘。现在，你需要让他们合作完成一道菜。你可以告诉他们“把蔬菜切成小块，然后炒熟”，他们会根据你的指令，选择最适合的厨师来完成每一步。这个系统就像SAME模型一样，它能根据厨房里的情况（比如食材、工具）和你的指令，自动决定哪个厨师该出场，确保菜做得又快又好。这样，厨房里的厨师们就能像一支灵活的团队，合作无间，完成各种复杂的菜肴。这种智能的调度机制让整个厨房变得更高效、更聪明，也更像一个有自己想法的团队。
ELI14 Explained like you're 14
你可以把这个技术想象成一群超级聪明的机器人队伍，每个机器人都擅长某一方面，比如一个专门找东西，一个专门跟着指令走。现在，如果你让他们一起完成任务，他们会根据情况自动决定谁来做什么。比如，你让他们找一个白色的陶瓷水槽，机器人会根据环境和指令，选择最擅长找水槽的机器人来完成。这个系统就像一个聪明的指挥官，知道什么时候让哪个机器人出场，确保任务顺利完成。它可以在不同的场景中灵活应对，不管任务多复杂，都能找到最合适的“机器人”帮忙。这让机器人变得更聪明、更灵活，也更像人一样会思考和调整策略。
Abstract
The academic field of learning instruction-guided visual navigation can be generally categorized into high-level category-specific search and low-level language-guided navigation, depending on the granularity of language instruction, in which the former emphasizes the exploration process, while the latter concentrates on following detailed textual commands. Despite the differing focuses of these tasks, the underlying requirements of interpreting instructions, comprehending the surroundings, and inferring action decisions remain consistent. This paper consolidates diverse navigation tasks into a unified and generic framework -- we investigate the core difficulties of sharing general knowledge and exploiting task-specific capabilities in learning navigation and propose a novel State-Adaptive Mixture of Experts (SAME) model that effectively enables an agent to infer decisions based on different-granularity language and dynamic observations. Powered by SAME, we present a versatile agent capable of addressing seven navigation tasks simultaneously that outperforms or achieves highly comparable performance to task-specific agents.