Title: C-NAV: Towards Self-Evolving Continual Object Navigation in Open World
Venue: NeurIPS 2025
Accepted: yes
Keywords: Embodied AI

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Abstract: Embodied agents are expected to perform object navigation in dynamic, open-world environments. However, existing approaches typically rely on static trajectories and a fixed set of object categories during training, overlooking the real-world requirement for continual adaptation to evolving scenarios. To facilitate related studies, we introduce the continual object navigation benchmark, which requires agents to acquire navigation skills for new object categories while avoiding catastrophic forgetting of previously learned knowledge. To tackle this challenge, we propose C-Nav, a continual visual navigation framework that integrates two key innovations: (1) A dual-path anti-forgetting mechanism, which comprises feature distillation that aligns multi-modal inputs into a consistent representation space to ensure representation consistency, and feature replay that retains temporal features within the action decoder to ensure policy consistency. (2) An adaptive sampling strategy that selects diverse and informative experiences, thereby reducing redundancy and minimizing memory overhead. Extensive experiments across multiple model architectures demonstrate that C-Nav consistently outperforms existing approaches, achieving superior performance even compared to baselines with full trajectory retention, while significantly lowering memory requirements. 
The code will be publicly available at \url{https://bigtree765.github.io/C-Nav-project}.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis submission introduces a continual object navigation benchmark based on the HM3D and MP3D datasets, where agents are required to incrementally learn new object categories while preventing catastrophic forgetting. The proposed C-Nav framework mitigates forgetting through a dual-path mechanism that combines feature-space distillation to preserve representation consistency and feature replay to stabilize policy learning. Additionally, an adaptive experience selection strategy based on the Local Outlier Factor (LOF) reduces storage overhead. The framework is evaluated across four navigation architectures using SR and SPL metrics under staged learning settings.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n- The overall presentation of the work is clean and easy to follow.\n\n- The submission establishes a clear continual learning task setting and benchmark for object navigation, which provides a useful foundation for future research in this area.\n\n- The evaluations are thorough, with experiments across diverse architectures (Transformer, RNN, BEV, LLM), and the visualizations help illustrate performance differences across stages.\n\n## Weakness\n- It is unclear whether there is a real need for continual learning in a task with only 6 categories, such as HM3D. The necessity of constructing a staged continual learning setting under such a limited scenario may lack a clear correspondence to real-world applications. \n\n- There is a lack of discussion or justification for key hyperparameters, such as the loss weighting coefficients and the threshold used in the LOF-based frame selection.\n\n- The comparison is limited to classical continual learning methods, and does not include more recent or advanced approaches. \n\n- Qualitative trajectory analysis is absent, which limits intuitive understanding of behavior consistency and the retention of task-specific strategies across continual stages.\n\nQUESTIONS:\n- Would it be possible to report separate performance on old and new tasks at each stage? This might provide a more intuitive understanding of how the model adapts to new tasks and the extent of forgetting over time.\n\n- Why is the L2 distance constraint between features from different stages effective in preventing representation drift? Is there any theoretical or empirical justification supporting this design choice?\n\n- One observation is that while several baselines exhibit performance recovery from stage 3 to stage 4, C-Nav continues to decline. What factors might account for this trend, and does it suggest potential limitations in the current mitigation mechanism under longer task sequences?\n\nLIMITATIONS:\nThe author discussed the limitations on page 9.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the underexplored but important problem of continual object goal navigation, where an embodied agent must incrementally learn to navigate to novel object categories without forgetting previously learned ones. The authors propose a new benchmark split for ObjectNav tasks on HM3D and MP3D, with objects introduced in multiple stages. To mitigate catastrophic forgetting, the authors propose a dual-path anti-forgetting strategy, combining (1) feature distillation to stabilize the visual encoder and (2) feature replay based on latent representations. Additionally, an Adaptive Experience Selection (AES) module selects representative frames for memory storage using semantic outlier detection. This design results in a lightweight, modular solution to continual navigation\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths:\n- Interesting motivation: The problem setting is realistic and highly applicable to embodied agents deployed in open-world environments.\n\n- Comprehensive contribution: The paper offers a benchmark design, a novel anti-forgetting method,\n\n\n### Weakness:\n- The method section is dense and may be difficult to follow for readers unfamiliar with continual learning or distillation techniques. More diagrams or intuition would help.\n\n- The paper lacks visualizations or qualitative examples, such as trajectory comparisons which would help readers understand what the model is learning or how retrieval is affecting behavior.\n\n- The continual learning baselines used for comparison are somewhat dated, It would strengthen the evaluation to include more recent rehearsal.\n\nQUESTIONS:\n- Have you tried evaluating the agent across different stages on similar or re-occurring objects? For example, if \"sofa\" appears in stage 1 and again in stage 4 under different visual conditions, how well does the model preserve category identity or avoid overwriting knowledge?\n\nLIMITATIONS:\n- Lack of qualitative analysis and visualization: The paper primarily presents numerical results (SR, SPL) but lacks qualitative analysis such as trajectory visualizations, failure cases, or behavior comparisons. This makes it harder to interpret what the model has learned or how forgetting manifests in practice.\n\n- Insufficient explanation of figures: For example, Figure 1 lacks accompanying explanation or description in the main text, which may confuse readers unfamiliar with the setting. Better figure captions or integration into the discussion would improve clarity.\n\n- No analysis of stage-order sensitivity: The current results are based on one fixed order of object introduction. However, in continual learning, the order of tasks can affect performance and forgetting. Exploring stage permutation effects would improve the robustness claims.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a new benchmark for studying continual learning in object navigation. As a trial of solving this problem, the authors propose C-Nav, a framework addresses the challenge of catastrophic forgetting in embodied agents by proposing a dual-path anti-forgetting mechanism (feature distillation and feature replay) and an adaptive experience selection strategy. The authors evaluates C-Nav across multiple model architectures, demonstrating superior performance compared to existing continual learning methods.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n1. The paper addresses an underexplored yet critical challenge—continual learning in embodied object navigation—and proposes a novel dual-path framework with strong theoretical grounding and practical relevance.\n2. The introduction of a continual object navigation benchmark, built upon HM3D and MP3D datasets, fills an important gap and provides a valuable foundation for future research in this domain.\n3. The dual-path anti-forgetting strategy and adaptive experience selection are both innovative and well-motivated, with clear justifications supported by empirical and theoretical insights.\n4. Extensive experiments across diverse architectures (RNN, Transformer, BEV, and LLM-based models) and datasets demonstrate the robustness and generalizability of C-Nav, reinforcing its potential for real-world deployment.\n\n# Weaknesses\n1. Feature Consistency Loss Concern:\n- The justification for the feature consistency loss appears questionable. If the goal is to maintain feature encoding similarity with the original encoder, freezing the encoder would seem like a more straightforward solution. To strengthen the argument, the authors should include an ablation study comparing the proposed approach with a frozen encoder to demonstrate the necessity of loss_KD.\n2. Adaptive Experience Feature Selection:\n- The proposed adaptive experience feature selection mechanism is innovative and well-designed. However, it would be valuable to compare its performance against simpler alternatives—for example, selecting neighbors based on minimum feature distance and replacing the least relevant samples. Such a comparison would help assess the added benefit of the proposed method's complexity.\n\nQUESTIONS:\nPlease clarify the weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-03"}