C-NAV: Towards Self-Evolving Continual Object Navigation in Open World
Authors: MingMing Yu
Fei Zhu
wenzhuo liu
Yirong Yang
Qunbo Wang
wenjun wu
Jing Liu
Venue: NeurIPS 2025 poster ()
Keywords: Embodied AI
Continue Learning
Primary area: applications

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}.

C-NAV: Towards Self-Evolving Continual Object Navigation in Open World
Ming-Ming Yu', Fei Zhu?t, Wenzhuo Liu Yirong Yang', Qunbo Wang"*, Wenjun Wu Jing Liu
Beihang University, Centre for Artificial Intelligence and Roboties, HKISI-CAS
Institute Automation, Chinese Academy of Sciences
University of Chinese Academy of Sciences
Hangzhou International Innovation Institute, Beihang University, Beijjig Jiaotong University mingmingyu@buaa. edu. cn wanggb6@outloox. com
Abstract
Embodied agents are expected to perform object navigation in dynamic, open-wotee world environments. However, existing approaches typically rely on static trajectories and fixed set of object categories during training, overlooking the real-world require- ment 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. tackle this challenge, we propose C-Nav, continual visual navigation framework that integrates two key innovations: (1) A dual-patt anti-forge horr-forgettig mechanism, which comprises feature distillation that aligns multi-modal inputs into consistent representation space to ensure representation consistency, and feature replay that retains temporal features within the action decoder 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 compared to baselines with full trajectory retention, while significantly lowering memory requirements. The code will be available ltttps://bittreee github. Nav----aaveeeee
Introduction
is fundamental capability for embodied agents and serves prerequisite for any meaningful interaction, making it central topic in recent research. typically depend on pre-trained models [T [3] operating under the assumption of complete access to the training dataset and a fixed set object categories. However, these approaches generally lack continual learning ability [14] and are vulnerable to catastrophic forgetting, which restricts their applicability in dynamic, apen-world applications [15]. contrast, practical deployment demands that agents adapt continuously to new object categories and evolving user instructions. Addressing this need requires agents to incrementally refine their navigation capabilities by learning from new data, without revisiting all previously seen data retraining the model from scratch.
Successful navigation to target object [
Current state-of-the-at methods 6 7
and large-scale demonstration trajectories 7 I0
To enable the acquisition of new skills without forgetting previously learned knowledge, numerous studies have achieved promising results in uni-modal settings like image classification [6 17
*Proeect lead.
*Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Navigation for old task
Find table Find the sota Hind the
Hind the sink Find the stool
Find the Find
Fin
Fil en
Find th
Find the stool
Continual Object Navigation
Baseline
Navigation capability common objects
Navigation capability of new objects
Navigation capability
C-Nev
arbitrary objects
Fiid Find
Stage
Stage
Figure 1: Continual Object Navigation: The robot must continually learn from new data while retaining its ability navigate previously seen object goals. After training across multiple phases, the baseline model suffers from representation drift and policy degradation, leading to a loss of navigation capability previously learned tasks, whereas C-Nav enables cumulative knowledge retention across phases.
However, their application to embodied decision decision-making tasks, particularly object-goal navigation, remains largely unexplored. This might be because the task requires long-horizon decision- making and the integration of diverse multi-modal inputs, including RGB images, depth images, pose information, and textual instructions. Moreover, single training trajectory can span hundreds of steps, which significantly increases the complexity of the learning process. To the best of our knowledge, continual learning in the context of object navigation has not been previously studied. To advance this important yet under-explored area, it is necessary to propose a comprehensive evaluation framework assess the continual learning capabilities of object navigation models.
In related embodied navigation systems 66 7 2, I8 9 10), the multi-modal encoder is often used for processing egocentric visual inputs, pose information, and other sensory signals to build a semantic representation of the environment. Simultancously, the action decoder integrates tem- poral observations to predict navigation actions. As the agent is exposed new tasks time, distributional shifts in sensory inputs and action patterns introduce biases into the model compo- nents, leading representation drift and policy degradation. This results in catastrophic forgetting of previously acquired knowledge, as illustrated in Figure [ The agent often becomes capable of semantic exploration only for objects in the current task, losing its ability navigate goals learned earlier. Although naive data replay (19] has demonstrated potential in mitigating catastrophic forgetting, they face several notable limitations when applied to navigation tasks characterized by long sequences of temporally and spatially correlated observations: (I) storage overhead scales quickly with the number of tasks, making memory management increasingly costly; (2) retaining continuous scene observations raises privacy concerns, as these may inad inadvertently reveal sensitive spatial information; and (3) the reliance on fine-grained, atomic-level action representations results in substantial redundancy across adjacent frames, leading to inefficient memory usage and the replay of repetitive experiences.
To advance embodied AI research in realistic settings, we introduce a continual object navigation benchmark built upon the HM3D 20 and MP3D (2T) datasets using the Habitat simulator (22).
This benchmark enables a comprehensive evaluation of mainstream navigation model architectures
(including Bev-based [9, RaN- (23). Transformer-
[18], and LLM-based models (8) and several widely adopted continual learning techniques such as LoRA [24], model merge (25), LwF (26),
and naive data replay [9.. Further, propose C-Nav, continual object navigation framework that incorporates a dual-path forgetting mitigation strategy. Specifically, feature distillation path employed to ensure representational consistency across multi-task, multi-modal encoders, while feature replay path is designed to preserve decision stability within the policy decoder. This architectural design addresses the representational drift that occurs in both the encoder and policy modules as the agent encounters new tasks, while avoiding the storage overhead and privacy risks associated with raw trajectory retention. To mitigate the high visual redundancy inherent in navigation trajectories, C-Nay introduces adaptive experience selection module. This module treats keyframe

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selection as an outlier detection problem in the learned representation space and leverages the Local Outlier Factor (LOF) (27] algorithm automatically identify critical navigation moments, such as "goal discovery" or "spatial transitions." In summary, our contributions include:
To evaluate the continual learning ability of agents in dynamic task settings, we establish a continual object goal navigation benchmark, enabling a systematic assessment of state-of-the- art navigation architectures and continual learning strategies.
We propose C-Nav, a continual Object Navigation framework that mitigates catastrophic forget- ting via dual-path strategy, enforcing representation consistency through feature distillation and policy consistency through feature replay, without relying on extensive raw trajectories.
We develop an adaptive experience selection mechanism that identifies and retains informative features of navigation frames by leveraging representation-space outlier detection, significantly reducing memory redundancy while maintaining policy performance.
Extensive experiments verify the effectiveness of C-Nav across architectures and continual learning methods, demonstrating superior performance in both accuracy and efficiency.
2 Related Work
2.1 Object Goal Navigation
The task of locating a specified object in an unknown environment is fundamental to robotic systems. Current approaches to object-goal navigation can be categorized into two types. (1) The first category focuses on Zero-Shot Object Navigation [28 29
4
3
30 These methods explicitly construct
37 for reasoning to select the most valuable frontier or waypoint for exploration. For instance, in Cow [29]. the robot explores the
maps and utilize VLMs B1 32
33
34 or LLMs 35
36
nearest frontier point until the target is detected using CLIP features [1 and open-vocabulary
object detectors [33. ESC [5) L3MVN (38), and Voronav 3 leverage LLMs for reasoning and pocision-making to enhance performance. VLFM [4] employs a VLM to assign semantic values to the map based on first-person observations and textual prompts, selecting the highest-scoring frontier. These approaches exhibit strong zero-shot generalization and avoid catastrophic forgetting, as they do not update model parameters. However, they rely on complex reasoning pipelines and manually designed exploration rules, making them cumbersome and computationally costly. Moreover, their inability to fine-tune newly collected navigation data limits task task-specific adaptation, often resulting in suboptimal performance in complex or novel environments. (2) The second category involves using pre- pre-trained visual encoders transform first-person observations into visual vectors, which are then processed by a navigation policy trained via large-scale imitation or reinforcement learning 66 7
8
2
9 [8,
10 39 40). thereby equipping agents with semantic navigation capabilities.
These approaches, often trained via imitation or supervised learning, provide robust performance for object-goal navigation tasks by incorporating large-scale datasets. However, these methods typically assume fixed environments and task categories, lacking the ability to learn continually. As a result, they struggle to adapt to dynamic settings where new objects and tasks emerge over time and are prone to catastrophic forgetting, which limits their generalization to open-worke world scenarios. Recent advances such as FSTTA 41] and NavMorph 42) enhance test-time adaptability through fast-slow gradient updates or self-evolving world models, but primarily focus on fine- grained, step-by-step navigation. In contrast, our work addresses moare--grained. long-horizon navigation, where the agent interprets high-level object-goal instructions to perform long-range exploration. Moreover, unlike prior methods that train static datasets, our framework progressively introduces new object categories across multiple training stages, enabling continual skill acquisition. To this end, we propose C-Nav, a continual navigation framework that equips agents with the ability to incrementally learn new capabilities and adapt to novel settings without forgetting previously acquired knowledge.
2.2 Continual Learning
also known as incremental learning or lifelong learning, has attracted significant attention in recent years. Existing methods mainly focus on image classification. Regular- ization methods 226 4 aim to prevent catastrophic forgetting by constraining the changes in model parameters during learning. Data replay methods [45 46), which store and reuse old data, can retain
Continual learning 14
3

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16
Multimodal Encoder
Feature Fusion
History
Action Decoder
RNN
Transarmr
Units
RNN-based
Bev-based
Image Encoder
Depth Encoder
Text
Prompt
Encoder
Transforerr
LLM
BEV
features
stool
image
Depth
Pose
Action
Object
Transtormer-assed
LMM-based
_Current..
Figure 2: Different navigation model architectures. According to the action decoder, they can be categorized into a) RNN- ONN-Based, b) Bev-Based, c) Transformer-Based. and d) LLM-Based.
prior knowledge but introduce additional storage and privacy concerns 47..
Architecture- -basd meth-
ods [88] add new submodules for each task, leading to an increase in model parameters as the learning
progresses. Recently, some studies 49 50 51 52] have explored continual learning of multimodal
large language models with parameter-efficient fine-tuning techniques (e.g.. LoRA [24] [53, prompt tuning (54)) to facilitate continual fine-tuning without significant computational overhead. However, these methods do not extend well to embodied intelligence, especially in long-range, multimodal ObjectNav tasks. In contrast, embodied navigation poses greater challenges, requiring agents to process multimodal inputs and adapt to evolving environments and goals. Existing continual learning techniques fall short in addressing such complexity, particularly long- long-horizon, multimodal tasks demanding both spatial and semantic reasoning. This gap motivates our C-Nav framework, which enables continual learning for visual navigation, with a focus on long-horizon tasks, multimodal integration, and dynamic adaptation in real-world settings.
3 Problem Definition and Benchmark
Problem Setting. We propose Continual- Saaiiuual-OjeeeNaav. where an embodied agent must incrementally master navigation skills through K sequential tasks. Each task & E {l,..., K} introduces distinct set of goal object categories Cg. with strict disjointness between tasks (C, n nc, = 0 for all # j). The agent receives training data Dr = {(*,, ot ( ) at each stage, where sf denotes a 3D indoor environment, of E C₁ specifies the target object category, and T. = =(of,, of provides expert demonstration trajectories. The observation of,, integrates egocentric RGB-D sensing, agent pose (positiio/oorentaation)), action history, and object name. The action a², is selected from a discrete action space A [move_forwanee turn_left, turn_right, look_ dook stop].
Evaluation. We evaluate the agent at the end of each training stage k. using test set of unseen enviromments S₁ and object goals sampled from the cumulative category set C1.A = U Cy. This setup requires the agent to retain and apply previously learned navigation skills while adapting new categories. Each episode starts from a random position, and the agent must navigate to a target object category с Clas. A success is recorded if the agent issues a stop action within specified distance to valid instance of the target object within 500 time steps. We report the most widely used metrics: Success Rate (SR) and Success weighted by Path Length (SPL), averaged across all categories in C1sk. SPL measures the efficiency of the agent's 's trajectory by comparing it to the shortest possible path from the starting position to the nearest valid instance of the target object category.
Model Architecture Notation. As illustrated in Figure 2 the agent policy, shared across different architectures, can be parameterized as composition of two learnable modules: a multimodal encoder 0 - Rd, which maps observations to compact feature representations h, = for)) and an action decoder that predicts navigation actions conditioned on both the current encoded feature and the trajectory history. At each time step t, the decoder receives hitt = (o)) ...,,,........ f(o))} and produces distribution over actions TTa,//iz))
Dataset. We adopt two widely used object goal navigation datasets: ObjectNav (HM3D) consists of 2,000 episodes sampled from 20 validation scenes in the HM3D dataset, covering 6 object categories. ObjectNav (MP3D), introduced the Habitat 2020 Challenge, contains 2, 195 episodes from 11 MP3D validation scenes, spanning 21 object categories. For model training, we utilize human demonstration trajectories collected via Habitat- Web [10] and PIRL. (7). The HM3D training set contains 75,488 trajectories, while the MP3D training set includes 59,604 trajectories after filtering

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Table I: Continual-ObjectNav splits for MP3D and HM3D.
MP3D
HM3D
Type
Stuge1
Stage2
Stuge3
Stuge4
Total
Stagel
Stage2
Stuge3
Stugel
Total
Category
12
3
3
3
3
Trajectory (training data)
40,608
7,306
7,484
4,206
59,604
36.667
13,640
13,182
11,999 2,000
75,488
Episodo (evaluation data)
1.783
1,934
2,039
2.195
2,195
1,343
1,624
2,000
RGB
Cup
Depth
LOF
Action
Pose
Feature Buffer
Text
Stool
Full RGB Inputs
Key Framers
Adaptive Experiennceeelecciion
RGB
Mu itimdda E Ecader fe-1
Depth
Move Forward
Tock
10s8go
Action
Pase
Mu ltmadall Encoder
Action Decoder
Text
bed
fa
Multimodal Inputs
Dual-P ath Anti-F Foi--Forgettng
Figure 3: Overview of the proposed C-Nav framework for continual object navigation. It consists of two key components: (1) adaptive experience selection, which identifies semantically meaningful keyframes via LOF in the representation space, reducing storage and redundancy: and (2) dual-path anti-forgetting, which mitigates representation and policy drift through a feature distillation path (bottom left) and feature replay path (bottom right), ensuring stability across encoder and decoder modules. For each task, C-Nav retains sparse trajectory features in the deep feature space.
out samples involving 6 synthetic objects. To adapt the continual Object Navigation setting, divide the object categories and corresponding trajectories into four incremental learning stages. The detailed splits are shown in Table [ and full list of object category assignments per stage can be found in the supplementary materials.
The Proposed Method: C-Nav
To address the continual object navigation task effectively, propose method described in this section. The overall framework of our approach is illustrated in Figure. 3
4.1 Dual-Path Anti-Forgetting
In continual learning of object navigation, catastrophic forgetting be attributed to representational drift in both the multimodal encoder and the action decoder. As the model sequentially learns new tasks, the distribution of learned features may shift, resulting in degraded performance earlier tasks. To address this, we propose a dual-path anti-forgetting strategy that constrains both the feature extraction and policy prediction stages.
Representation Consistency with Feature Distillation. The multimodal encoder integrates visual and goal-related information. To prevent drift in the learned feature representation, apply feature- level distillation. Specifically, we enforce the consistency between the old and current encoder outputs by minimizing the & distance between their extracted features across time:
LRD
Σ [=1
[l (c(0))1
(I)
5

---

where f denotes the frozen encoder from previous task, and fa is the current encoder. Both take as input the observation o₁, ensuring multimodal alignment preserved during continual learning.
Policy Consistency with Feature Replay. To mitigate the policy forgetting during continual learning, replay stored trajectory features of previous tasks along with their corresponding action labels to maintain policy consistency. The feature replay loss is defined as:
PR
1 L
Σ 1=1
we log Ta (11:)s We = 11+ 11 #017
(2)
where hu E IRd represents the 1-th frame feature stored in the feature buffer, and denotes the current action decoder. The weighting factor y [I0] emphasizes action transitions by assigning higher importance to time steps where the expert action differs from the previous one. This objective function ensures that the policy maintains consistent interpretations of previously learned features while adapting to new tasks. The gradient updates through this loss term preserve the mapping between historical observations and their originally learned actions, thereby mitigating catastrophic forgetting in the continual learning scenario.
4.2 Adaptive Experience Selection
In continual object navigation, replaying previously collected trajectories is critical for mitigating catastrophic forgetting. However, storing all frames in trajectory is memory-intensive and often wasteful, many frames are visually redundant and contain little novel information. From data distribution perspective, frames that exhibit significant semantic shifts tend to be rare and thus can be considered outliers. These frames often correspond to moments such as entering new spaces, approaching target objects. or reaching decision points. Therefore, we propose to transform the task of selecting salient experience points into the detection of outliers within the feature space of visual observations. Specifically, given a trajectory observations using a pretrained CLIP model to obtain feature representations: v; = CLIP(RGB(()) for i = (1..... L.). Let dist(v V2) denote the Cosine distance in feature space:
{01, o2. Jr.). we first encode visual
v. V
d(v,, v2)
Base that, define the k-distance neighborhood: (v;) {v, d(vi, vj) < du dv:)). where dx(v.) is the distance to the k-th nearest neighbor. For each neighbor v, E (v::),. the reacha- bility distance is defined reach-dist, (V,, Vÿ) = max(dy(v) dvv), vz)). Using this, the local reachability density (LRD) of v, is the inverse of its mean reachability distance:
(v.)
[IN((V)))
Σ reach-dist, Vy) (v.)
(4)
Then, the LOF is calculated as the relative density of v, compared to its neighbors:
LRD)(V;)
LOF [V.(
Σ
(5)
LRD)(v;)
VyEN,(vV)
According this formulation, the set of selected keyframe indices is defined as I {i LOF) >
1}. where a higher (OF)(v;) indicates that the frame is considered as an outlier, typically corre- sponding a navigation state with significant semantic change. These keyframes are subsequently encoded through a multimodal encoder fa and stored in feature buffer to mitigate catastrophic forgetting in the action decoder during training.
4.3 Overall Learning Objective of C-Nav
Our continual navigation policy is optimized using a composite loss function that integrates supervi- sion from the current task. feature- sppace knowledge distillation, and feature replay consistency:
L = Lear ARD LKD + \FR LFR:
(6)
where ARD and ÄFR are weighting coefficients that balance the influence of feature-level knowledge distillation and feature replay loss, respectively. The current task i using behavior cloning with --.. inflection weighting [10: Leur & -/= we log Te (or ...

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Finetuning SR
LoRA -R
LwF SR
Merge -R
DR-5R
C-Nav SR
Finetuning - SPL
LoRA SPL
LWF SPL
Merge SPL
DP SPL
-
HM3D-RNN
HM3 D-Trans
HM3D-Bev
HM3D-LLM
60
Werrerrage (%)
20
stage2
stage3
staged
MP3D-ANN
MP3D-Trans
MP3D-Bev
MP3D-LLM
40
Pereanage 1%a1
30
20
10
Stagel
stage2
Stage5
staget
Stagel
Stage2
stages
staged
Figure 4: Results of SR and SPL on the MP3D and HM3D datasets. Solid lines represent SR, while dashed lines represent SPL.
Table 2: Performance comparison on the HM3D benchmark. DP indicates data replay, and Merge denotes model merging. Avg reports the average performance across all stages, while Last reflects the performance at the final stage.
HM3D-RNN
HM3D-Tram
HM3D-Rev
HM3D-LIM
Methods
Avg
Last
Avg
Last
Avg
Last
Avg
Last
SR
SPI.
SR
SPI
SR
SPL.
SR
SPL
SR
SPL.
SR
SPI.
SR
SPL.
SR
SPI.
Finetuning
32.8
13.0
21.3
31.4
12.9
7.6
32.0
13.3
20.8
8.2
28.4
11.3
16.4
LoRA
34.0
36.3
24.1
LwF
34.4
13.5
25.1
8.5
31.7
12.6
19.2
32.6
13.1
21.7
8.1
26.2
10.8
11.7
4.5
Merge
40.8
16.4
20.4
6.5
45.1
18.4
24.5
7.2
45.0
17.8
18.5
4.6
42.5
16.4
19.9
5.6
DP
44.1
18.6
33.6
13.2
52.7
22.1
39.6
16.5
53.2
23.0
44.2
18.3
52.2
21.2
40.9
15.9
C-Nav
50.0
21.0
40.3
16.5
55.8
20.2
56.3
24.0
19.6
52.2
23.1
5
Experiments
5.1
Experimental Setup
Implementation Details. We implement our multi-modal encoder using CLIP-ResNet50 [1]] for visual encoding and a PointNav-pretraitn work 23 91,
ResNet-50 (55) for depth encoding. Following previous we keep both encoders frozen during training. The feature fusion implemented by the feature concatenation. We train our model using Adam optimization with linear warmup over 1. 1,000 steps reach our initial learning rate of 3 10-4, followed by linear decay. We train each task stage for 25 epochs with batch size of 32. For action prediction, the RNN-based model employs 2-layer LSTT (56) architecture. The transformer-base
and Bev-based 9 decoders utilize a 4-layer transformer 557 as the action decoder with dropout rate of 0.1. For the L.MM-baedd approach, we adopt Qwen2-0.5B [37] as the action decoder, incorporating six special tokens to represent atomic actions. We set the inflection weight 3.48 and configure our loss balance weights ÅKD and ÄFP to 5. All experiments are conducted using two NVIDIA A6000 GPUs. Additional implementation details are provided in the supplementary materials.
Comparison Methods. We compare our method against several representative continual learning strategies across four backbone architectures. Specifically, we evaluate the following baselines. Naive Fine-tuning serves as the simplest approach, where models are sequentially fine-tuned on each stage without any mechanism to mitigate forgetting. LoRA 24 introduces low-rank adaptation modules during training: after each stage, the LoRA branches are merged into the main model for inference. LwF 26 applies knowledge distillation by computing the KL divergence between the logits of the
7

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Table 3: Performance comparison of different methods MP3D benchmark.
MP3D-RNN
MP3D-Trans
MP3D-BeV
MP3D-LLM
Methods
Avg
Last
Avg
Last
Avg
Last
Avg
Lasi
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
Finetuning
19.4
5
14.1
2.1
20.1
5.3
19.9
3.7
27.4
6.0
23.5
3.5
14.7
4.0
3.1
0.5
LoRA
24.2
6.7
23.5
5.9
29.5
7.6
27.1
7.1
31.1
8.2
27.1
6.0
LwF
22
5.6
20.7
5.3
2.8
27.6
5.9
3.3
14.6
3.9
6.4
Merge
30.3
8.4
17.3
33.8
10.3
25.7
41.7
10.9
36.0
6.9
26.5
7.4
14.6
3.7
DP
33.8
9.3
30.3
37.7
10.8
37.2
10.1
41.8
11.4
38.1
9.8
26.3
7.7
22.1
6.4
C-Nav
36.4
11.0
34.3
9.7
38.1
12.1
36.8
11.9
42.4
12.3
39.0
11.2
36.1
10.8
35.0
10.7
current model and those of the previous one, encouraging the model to preserve past knowledge. Model merge preserves prior knowledge by linearly interpolating the weights of the current and previous models using a 0.7/0.3 ratio. Lastly, data replay stores p = 80 original trajectories per object category, which are reused in subsequent training stages to mitigate forgetting. Additional implementation details are provided in the supplementary materials.
5.2 Experimental Results and Analysis
Main Results. We compare several baseline methods and model architectures for Continual- ObjectNav task. As shown in Figure naive fine-tuning leads to severe catastrophic forgetting across all four architectures, with an average decline of approximately 40% in navigation success rate on previously seen categories by the final stage. Additionally, the results show that the Bev-based architecture consistently outperforms others. Mcanwhile, LLM- based models do not exhibit clear performance advantage, likely due to limited training trajectories. The LwF method demonstrates limited effectiveness across all models. In contrast, LoRA and model merge help mitigate forgetting by either reducing the number of trainable parameters or integrating knowledge from previous models. Notably, LoRA shows a significant advantage when applied to LLM-based models compared to other model architectures. Data replay achieves effective performance retention, but it incurs substantial storage overhead and raises privacy concerns. As shown in Table and Table 3 the proposed C-Nav generalizes well across various architectures and datasets. Specifically, compared to data replay, C-Nav achieves average SR improvement of 3.35% MP3D and 2.75% on HM3D across four model architectures, while requiring lower storage costs. detailed breakdown of performance on old new tasks each training stage is provided in Appendix C.T illustrating how C-Nav balances retention of prior knowlest wewle with adaptation to new tasks. These results demonstrate the effectiveness of our dual-path forgetting mitigation mechanism, which ensures consistent representation and policy learning as the agent encounters new tasks.
Table 4: Ablation studies of dual-path anti-forgetting. KD denotes feature distillation, and FP denotes feature replay.
HMBD-RNN
HM3D-Trame -raas
HM3D-Bev
HM3D-LLM
Methods
Avg
Last
Avg
Last
Avg
Last
Avg
Last
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
w/o KD
28.2
11.7
16.9
6.6
31.4
13.2
20.2
8.1
32.5
13.9
21.9
8.9
33.6
13.4
19.9
7.3
w/o FP
37.9
15.6
27.9
10.7
45.9
21.9
32.6
14.5
38.9
16.5
26.7
10.3
42.7
17.2
30.2
11.6
All
50.0
21.0
40.3
16.5
55.8
24.8
46.5
20.2
56,3
24.0
46.5
19.6
52.2
23.1
42.2
19.3
MP3D-RNN
MP3D- Trans
MP3D- Bev
MP3D-LIM
Methods
Avg
Last
Avg
Last
Avg
Last
Avg
Last
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
w/o KD
16.9
4.7
10.4
2.0
20.6
5.8
21.2
4.6
26.1
6.6
23.5
5.2
25.7
6.4
24.0
4.8
w/o FP
25.9
8.1
22.0
7.3
27.2
8.9
22.7
8.5
31.3
9.6
29.2
9.95
28.9
9.0
24.7
8.0
36.4
11.0
34.3
38.1
12.1
11.9
42.4
12.3
11.2
36.1
10.8
35,0
10.7
Ablation Studies of Dual-Path Anti-forgetting. To assess the contribution of each component in our proposed dual-path fnti-forgetting mechanism, conduct ablation studies by separately removing
8

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the feature-space alignment loss and the feature replay loss in the action decoder. As presented in Table 4 and Appendix C.4, the removal of either component leads to significant performance degradation. Specifically, eliminating the feature-space KD) loss results in average SR drop of 22% and 16% across four model architectures on the HM3D and MP3D datasets, respectively. Similarly, removing the feature replay loss from the action decoder causes reduction of 12% and 10% on the same datasets. These results highlight the complementary roles of the two components: the feature- space constraint stabilizes representation learning, while feature replay ensures consistent decision- making under distributional shifts. Additionally, we further examine representation consistency in continual learning. As detailed in Appendix C.2 compare fully freezing the multimodal encoder against selectively fine-tunne tine-tuning only the mnodality-specinee projectors with consistency regularization to softly align old and new feature spaces. The results show that fully freezing the encoder degrades performance, whereas selective fine-tuning of projectors under consistency constraints enables the model learn representations that remain effective for both previous and new tasks.
A Ablation Studies Adaptive Experience Selection. To validate the effectiveness of the proposed adaptive experience selection in C-Nav, we compare it with uniform sampling baseline that assumes redundancy among adjacent frames. For fair comparison, both methods replay trajectories truncated to 50% of their original length. As shown in Table 5 and Appendix C.5 our method consistently outperforms uniform sampling across model architectures and datasets, achieving SR improvements of 3.65% on HM3D and 3.2% on MP3D. Notably, compared to full-length feature replay in C-Nav, SR drops only marginally by 1.9% and 1.3%, despite using half the data. In addition, compared to full data replay,
adaptive method achieves average SR improvement of 0.8% on HM3D and 2.0% on MP3D across four model architectures. These results demonstrate the efficiency and effectiveness task.
of our adaptive strategy in preserving critical experiences for the
Table Ablation studies of adaptive experience selection. "Uniform", "Adaptive", and "Full" refer to uniform sampling, adaptive sampling, and no sampling, respectively.
HM3D-RNN
HM3D-Trans
HM3D-Bev
HM3D-LLM
Methods
Avg
Last
Avg
Last
Avg
Last
Avg
Last
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
Uniform
43.2
19.7
32.2
15.0
50.5
22.0
40.5
17.6
49.4
21.7
37,6
16.5
47.7
22.4
37.0
18.4
DP (Full)
44.1
18.6
33.6
13.2
52.7
22.1
39.6
16.5
53.2
23.0
44.2
18.3
52.2
21.2
40.9
15.9
Adaptive
47.3
20.7
36.1
16.0
53.7
23.2
42.5
18.4
52.8
22.4
42.1
18.0
51.6
23.8
40.1
19.4
Full
50.0
21.0
40.3
16.5
54.7
24.3
46.5
20.2
56.3
24.0
46.5
19.6
52.2
23.1
42.2
19.3
MP3D-RNN
MP3D-Tram Trans
MP3D-Bev
MP3D-LLM
Methods
Avg
Last
Avg
Last
Avg
Last
Avg
Last
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
SR
SPL
Uniform
30.5
10.3
24.5
8.2
35.2
11.4
31.9
9.7
35.0
11.0
29.2
9.7
34,0
11.1
31.4
10.9
DP (Full)
33.8
9.3
30.3
6.9
37.7
10.8
37.2
10.1
41.8
11.4
38.1
9,8
26.3
7.7
22.1
6.4
Adaptive
34.1
11.4
38.4
10.1
38.4
12.6
36.6
11.7
40.9
12.3
38.8
11.3
34.2
11.2
32.4
10.7
Full
36.4
11.0
34.3
38.1
12.1
36.8
11.9
42.4
12.3
39.0
11.2
36.1
10.8
35.0
10.7
Moreover, Figure presents ablation study of different ex- perience selection strategies MP3D-Traas across all training stages. First, we compare our method against uniform sam- pling and clustering- pling, both using half-length tra- jectories. Our approach con- sistently outperforms both base- lines because it explicitly pre- serves semantically salient frames, such as those captured at decision points or near target objects. These frames often reside outlier regions of the feature space and are typically discarded by conventional samplers. Second, reduce the key-frame selection ratio, Remarkably, method
sam-
20
Shyp1
9age3
Stagel
Figure 5: Ablation experience selection and retention ratio MP3D with a Transformer-bas navigation architecture.

---

Dred DXD
Dreck (9)
Coasten
-8- WW.
-A- Fressieg
Fradthe bed
35
342
203
5uge2
Sajed
Sajed
blapet
2inge2
Slage
Figure 6: Comparison with the Zero-Shot Method.
Figure 7: Sensitivity to stage order on MP3D.
Figure 8: Qualitative navigation results.
maintains strong performance even very low retention rates, achieving comparable or better results while using only 20% of the memory required by the clustering based or uniform sampling baselines. This demonstrates the high efficiency and robustness of our approach.
Comparison with the Zero-Shot Method. We reproduce VLEM as a zero-shot baseline and evaluate it against C-Nav over four continual learning stages in Figure 6 VLFM selects waypoints using a pretrained vision-language model on an explicit map and executes navigation with a fixed reinforcement learning policy. Because its parameters are frozen, it avoids catastrophic forgetting and maintains stable but limited performance. This limitation stems from its inability to learn from new trajectories, leaving it constrained by the static priors of the large model. In contrast, C-Nav is an end-to-end trainable system that continuously improves from experience, adapts to new tasks, and mitigates forgetting.
Ablation Study on Stage Order Sensitivity. To evaluate the robustness of C-Nav to variations in task sequence during continual learning, we conduct ablation studies under three different stage orderings. As shown Figure the minimal performance variation across orderings further validates the effectiveness of our Dual-Path Anti-Forgetting mechanism. This insensitivity to task sequence is crucial for real-world deployment, where object categories may be encountered arbitrary order.
Case Study. Figure g shows navigation trajectories of C-Nav and the Finetuning baseline in an unseen HM3D scene, where the target object (bed) was introduced in an earlier training stage. Both models were trained sequentially over four stages, with the sofa added as the target in the final stage. The baseline suffers from catastrophic forgetting and navigates toward a sofa despite the goal being a bed. In contrast, C-Nav successfully reaches the bed, demonstrating its ability to retain knowledge of previously learned categories. This highlights C-Nav's superior stability-pland paaaiity-llsstictty balance in continual learning.
6 Conclusion
In this work, we establish benchmark for evaluating continual learning in the context of object navigation. We assess various model architectures and mainstream continual learning methods. Building on this benchmark, we propose C-Nav for continual object navigation, a dual-pptll framework that mitigates catastrophic forgetting by jointly enforcing representation consistency through feature distillation and preserving policy consistency via feature replay. Additionally, we introduce an adaptive experience selection method to further reduce memory usage, ensuring efficient knowledge retention. Extensive experiments demonstrate that our approach outperforms existing methods, achieving superior performance across different architectures and tasks, while reducing memory overhead and mitigating forgetting more effectively than previous solutions.
Limitations. Despite the strong performance of C-Nav on the Continual-ObjectNee task, several limitations remain. First, real-world factors such as dynamic lighting and sensor noise may affect generalization, highlighting the need for validation on physical robots to assess robustness in real environments. Second, although C-Nav significantly reduces memory usage, its storage demands still grow linearly with task complexity. Future work may explore generative replay or trajectory-free approaches to further reduce memory overhead. Addressing these limitations is crucial for deploying continual navigation systems in practical applications.
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Acknowledgments and Disclosure of Funding
This research is supported by the Artificial Intelligence National Science and Technology Major Project (20237D0121 200),, the National Natural Science Foundation of China (62437001, 62436001, 62206279), the Key Research and Development Program of Jiangsu Province (BE2023016-3), the Beijing Natural Science Foundation (L252146), and the InnoHK program.
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A Dataset Details
A.1 Category Splits for Continual ObjectNav
HM3D Dataset. The object categories are split into four stages as follows:
Stage 1: bed, chair, plant
Stage 2: toilet
Stage 3: tv_ v_monitor
Stage sofa
MP3D Dataset. The object categories are divided into four continual learning stages:
Stage 1: bed, cabinet, chair, chest_ of drawers, cushion, picture, plant, sink, sofa, stool, table, toilet
Stage 2: shower, towel, tv_monioor
Stage 3: bathtub, counter.fireplaee
Stage clothes, gym cym_equipment, seating
A.2
Demonstration Trajectory Length Distribution for Continual ObjectNav
We visualize the trajectory length distributions across different training stages in Continual ObjectNav. as shown Figure and Figure IO For the HM3D dataset, the average trajectory lengths for Stage 1 to Stage 4 are 127.51, 142.53, 168.53, and 1 115.77, respectively. In contrast, the MP3D dataset exhibits longer trajectories, with average lengths of 182.79, 247.91, 227.10, and 315.75 across the four stages. The longer demonstration sequences in MP3D highlight the increased complexity of decision- makii,, making the Continual ObjectNav task more challenging.
Stage Trajectory Length Distribution (Count: 36667)
Stage Trajectory Length Distribution (Count: 13640)
0h
Moan: 127 St
15.%%
Moan:
14th
140%
12.0%
ath.
3.0%
46%
205
2.%-
Length
Lengthh
Stage Trajectory Length Distributiin (Count: 13192)
Stage Trajectory Length Dis Distribution
(Count: 11999)
23.0%
Poporten
12.5%
13m%
25%
00%g
1800
3.0%
200
Lerggh
Length
Figure 9: Demonstration trajectory length distribution per stage HM3D dataset.
B
Implementation Details
B.1 Details about the Model Architecture
For the BEV-based, transformer-based. and RNN-based models, the RGB, depth, pose, previous action, and object name inputs are transformed by linear projection layers into feature vectors with dimensionalities of 256, 128, 64, 32, and 32, respectively. In the Qwen 0.5B LLM-based architecture,
22

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Stage Trajectory Length Distribution (Count: 40608)
Stage Trajectory Length Distribution
(Count: 7306)
Mesuer 124.02
Modaan 183.00
6.5%
0.0
1.0%
2.56
100
410
100
0.0%
271
4.00
603
600
1000
Stage Trajectory Length Distribution 7484)
Stage Trajetoryy Length Distribution (Count: 4206)
Median: 106.3
227.10
6.5%
Modia: 220.00
315
Propeetion
6%
6.6
2,64
0.69
200
610
402
611
0.0% 201
Figure 10: Demonstration trajectory length distribution per stage in MP3D Dataset.
the RGB, depth, pose, and previous action inputs are projected into feature vectors of size 400, 400, 64, and 32, respectively. These vectors concatenated produce unified representation with total dimensionality of 896. The task prompt used for the LLM-based model is formatted as follows:
< sii_start// />aystem You are a smart navigation assistant, <lim_end < <li_start|>> |>> Your goal is to find the Kobject_name)) What is the next action?
<| 1mm__ee |
The learning rate for the LLM-based model is set to 0.0001. bfloat16 precision is adopted for efficient inference and reduced memory usage the LLM- --Meeddadddd navigation models. The weight decay is set to 0.1. Additionally, several special tokens are defined. Specifically, the action tokens include <STOP>, <MOVE_ <MOVE_FORWARD: <TURN LEFT>, observation token, < observation >>, is used to represent the environment' 's state in the model's input. All other hyperparameters are kept consistent with those used in the BEV- transformer- based, and RNN-based models. For the representation consistency loss in our Dual-Path Anti- Forgetting mechanism, we use the lz distance between old and current features on HM3D, and the squared f2 distance on MP3D.
STURN_EFF>> STURN_RIGHTT
FLOOK_UP> and
The
B.2 Details about the Compared Continual Learning Methods
LoRA: Uses a low-rank decomposition with dimension R 16, scaling factor of 2 R. and a dropout rate of 0.1.
LwF: Applics a KL divergence loss between the current and previous model logits with a coefficient of 0.2.
Model Merge: Linearly interpolates the weights of the current and previous models using 0.7/0.3 ratio.
Data Replay: Stores p 80 original trajectories per object category for experience replay.
Proposed C-Nav: Stores p 80 trajectory features and samples 50% of each trajectory for adaptive experience selection, ensuring fair comparison with data replay and uniform sampling methods.
B.3 The Pseudocode for C-Nav
The pseudocode for C-Nav illustrated in Algorithm T During the initial stage, the model trained on trajectories from the base object navigation task. Adaptive experience selection is then applied to identify important experiences, which are stored in the feature buffer subsequent learning stages, the model weights from the previous phase are loaded, and the model is optimized by combining
23

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the current task's 's behavior cloning loss with feature space distillation and feature replay losses. This approach ensures the consistency of the multimodal feature encoder and action decoder across different tasks.
Algorithm I C-Nav for Continual ObjectNav with Adaptive Sampling
Initialize: Multimodal encoder fo, decoder To, feature buffer B 0
2: for task & = to K do
3: Input: Dataset D& = {( ck +
4: if k then
5:
# #errform behavior cloning for initial task
6:
Compute Lcur
7:
Update fu. Tu by minimizing Leur
8:
else
9:
perform feature distillation
10:
Compute LXD (Eq. 1) between fx-1 and fe
11:
perform feature replay
12:
Compute LrR with B (Eq. 2)
13:
Update fk. Tu by minimizing Leur + AKDLKD + AFRLER (Eq. 10) end if
14:
15:
perform adaptive experience selection in deep feature space Randomly sample p trajectories from Dy: Tp { .......: Tpl
17:
for each trajectory in Tp do
Encode frames {vr} via CLIP
19:
Compute LOF scores (Eq. 3-5)
Select keyframes I (t LOFx(v)) > 1}
21: for each E I do
Store (fx (or) ar) in buffer B 23: end for
end for
25:
end for
C More Results
C.1 Old vs. New Task Performance per Stage
To provide a more intuitive understanding of how the model adapts to new tasks and the extent of forgetting over time, we report results on the HM3D dataset by separately evaluating performance on new tasks (i.e., target objects in the current stage) and old tasks (i.e., all previously seen target objects, averaged) in Table and and Table 7 These results reveal two key trends. First, Finetuning suffers from severe catastrophic forgetting. Although achieves reasonable performance on new tasks (e.g., 64.63% in Stage 4), its success rate old tasks drops below 10% after Stage 2. Second, C-Nav achieves a superior trade-off between stability and plasticity. While its new-task performance slightly lower than that of Data Replay in later stages, it substantially outperforms all baselines on old tasks. Notably, in Stage 4, C-Nav attains an old-task success rate of 42.61% %, which 9.7 percentage points higher than the best baseline (Data Replay at 32.9%). This demonstrates that C-Nav effectively mitigates catastrophic forgetting while preserving the ability to learn new navigation tasks.
Table 6: SR on new tasks across stages (HM3D).
Table 7: SR on old tasks across stages (HM3D).
Method Stage
Stage 2
Stage
Stage 4
Method
Stage
Stage
Stage 3
Stage
Finetuning
69.63
60.55
29.54
64.63
Finetuning
69.63
7.72
9.83
9.05
LoRA
69.63
60.55
38.08
69.15
LoRA
69.63
17.99
8.19
11.70
LwF
69.63
54.27
19.57
57.98
LwF
69.63
16.19
8.64
10,22
Merge
69.63
16.08
4.63
21.81
Merge
69.63
66.03
41.62
25.12
Data Replay
69.63
59.80
29.18
68.35
Data Replay
69.63
54.60
48.92
32.94
C-Nav
69.63
54.52
26.33
63.30
C-Nav
69.63
61.38
52.27
42.61

---

C.2 Additional Analysis on Feature Consistency Loss
Frozen
Unfrazen
14
12
SR (%)
SPL (%6
10
30
8
25
6
20
4
Stage
Stage
Stage
Stage 4
Stage
Stage
Stage
Stage
Figure 11: Ablation study: Freezing vs. unfreezing the multimodal encoder on MP3D Continual
ObjectNav. We conduct experiments using a munsformer-basee architecture.
To validate this design choice, conducted an ablation study on the MP3D Continual ObjectNav dataset by fully freezing the entire multimodal encoder. The results showed a performance drop across learning stages, confirming that full freezing harms generalization and underscoring the necessity of our consistency loss. Our feature consistency loss is designed to learn shared feature space that remains effective across both past and new tasks. Simply freezing the entire multimodal encoder []] may preserve old knowledge but limits the model's adaptability, especially when the original feature space does not sufficiently capture new task-specific semantics. Instead, we selectively fine-tune modality-specii
projectors within the encoder while applying consistency regularization to softly align the old and new feature spaces. This design allows the representations to stay connected, yet remain flexible enough to adapt new tasks. It strikes balance between preserving past knowledge and enabling plasticity for future learning.
C.3 Ablation on Loss Weighting Coefficient À (MP3D)
Table Ablation on À for loss weighting (MP3D).
Stage
Stage 2
Stage 3
Stage 4
SR
SPL
SR
SPL
SR
SPL
SR
SPL
39,88
13.32
38.00
12.25
35.41
11.00
36.22
10.85
5
39,88
13.32
38.37
12.29
37.32
10.97
36.81
11.90
10
39.88
13.32
36.66
11.80
36.68
11.45
25.29
9.51
We study the sensitivity of C-Nav to the loss weighting coefficient X, which balances the replay loss (for retaining old knowledge) and the current-task loss (for learning new tasks). Table g reports success rate (SR) and SPL across four sequential training stages on MP3D, using X E {1, 5, 10]; all other model configurations are identical. As increases, the influence of replayed data as regularizer grows stronger in later stages. While this helps preserve prior knowledge, excessively large value (e.g., A 10) over-constrains learning and hinders adaptation to new tasks, resulting in sharp performance drop in Stage 4 (SR= 25. 25.29%). On the other hand, X offers too little regularization, causing gradual forgetting of previously acquired skills. The setting = strikes an effective balance: it yields slightly better overall performance than retention of old tasks. Consequently. À = achieves the best trade-off between stability and plasticity and is adopted in all main experiments.
while maintaining strong
C.4 Results for the Dual-Path Anti- Anti-Forgetting Performance at Each Stage
To intuitively analyze the contribution of each component in C-Nav, we visualize the and SPL curves for each stage. As shown in the Figure 12 removing any component leads to significant drop in both SR and SPI. across all four model architectures and two datasets. Additionally, we report The results
the average success rate for each component across the four architectures in Figure 13
25

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reveal that the multimodal encoder is prone to bias across tasks, leading to more severe forgetting. Specifically, removing the KD component causes substantial performance degradation: SR drops by 22.15 and 15.92 on HM3D-SR-Avg and MP3D-SR-Avg. respectively, and by 24. 15 and 16.50 on HM3D-SR-Last and MP3D-SR-Last.
KD SPL
w/o FP SPL
SPL
HM4
HM3D-Trans
HM3D-Ber Bev
HM3D-LLM
60
60
Stage1
Stage2
Stage3
Staged
Stagel
Stage2
Stage3
Staged
Stagel
Stagez
Stage3
Stage4
Stagel
Stage2
Stage3
Stage4
MP3D-LLM
60
80-
20
Stage1
Stagee
Stage3
Staged
Stagel
Stage2
Stage3
Staged
Stagel
Stage2
Stage3
Staged
Stagel
Stage2
Stage3
Stage4
Figure 12: Results for the dual-path anti-forgetting performance each stage.
Average Success Rate Each Component Across Four Architectures
HM3D-5R-AVQ
HMWDD-RR-Lst
MP3D-5R-Avg
MP3D-SR-Last
53,575
43.875
38.250
36,280
Performance
41.350
Performance
29.350
Performance
28.325
24.650
31.425
22.330
19.780
19. 725
KD
wi FP
wio KD
wio FP
AB
wio KD
wio PP
AB
wio wio pr
Figure 13: Average success rate for each component across four architectures. HM3D-SR-AN and MP3D-SR-Ave denote the mean SR over all stages, while HM33-SR-LaS and MP3D-SR-Lant indicate the mean SR in the final stage, averaged across architectures.
26

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C.5 Results for the Adaptive Experience Selection at Each Stage
In Figure 14, we visualize the SR and SPL curves across different training stages under various sampling strategies. Our adaptive sampling method achieves performance comparable to training without any sampling. while clearly outperforming both uniform interval sampling and naive data replay approaches. As shown in Figure 15 the gains are particularly evident when compared to data replay: our method improves the average success rate by 0.8 and 2.0 on HM3D-SR-Avg and MP3D- SR-Avg, respectively, and by 0.625 and 4.625 on HM3D-SR-I Last and MP3D-SR-LAA Compared uniform sampling. our approach yields even larger improvements, boosting HM3D-SR-Last and MP3D-SR-Last by 3.375 and 7.3, respectively. These results demonstrate that adaptive sampling strategy not only maintains competitive overall performance but also significantly enhances learning stability and stngl-stage effectiveness. By selective revisitation of information-rich trajectory frame features, the model better retains knowledge across tasks, leading to more robust continual learning in navigation scenarios
DR(Full) DP(Full) SPL
Adaptive Adaptive SPL
Unitorm- SPL
Full SPL
HM3D-Trans
70-
40
30
30
20
20
26
20
10
10
10
20gg1
stag22
Stage3
St3ge4
Stage]
stage2
5age3
503g5
Sagel
50002
50003
20go4
500
50033
255
5.0
MP3D-RNN
50
MP3D-Tans
50
MP3D-Buv
50
MP3D-LLM
30-
30
30-
30-
20
20
20
Stage1
stago2
stage3
stage4
Stage2
stage2
stage3
stage4
Stage
Stago2
Stage3
Stagel
stage2
stage3
20g
Figure 14: Results for the adaptive experience selection at each stage.
Average Success Rate of Different Sampling Methods Across Four Architectures
HM3D SR Aug
HM3D Last
MP3D SR Avg
MP3D SR Last
362255
47.700
50.550
40.200
33.875
34.900
31.925
Parformance
Performance
Performance
Unifom
DP (ful
Adaptiss
Fuil
Unitorm
pP (ull
Adaprise
Fuil
Unifor
BP fuull
Adaptive
Fuil
Uniform
oe (ful)
Adaptive
Full
Figure 15: Average success rate for different sample methods across four architectures.
27


Reviews:
[{"review_id": "tDHfsrdTuQ", "reviewer": "Reviewer_xhkG", "summary": "This 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.", "questions": "Please clarify the weaknesses.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "# 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.", "was_revised": true, "final_justification": "I believe the paper’s most significant contribution is the introduction of a well-designed task setting for evaluating continual-learning capabilities in navigation agents. Prior work has typically assumed that agents are pre-trained once and then deployed directly, with little attention paid to post-deployment adaptation. In practice, continual learning is crucial: real environments often contain situations never seen during pre-training, and adapting to individual user preferences is something pre-training alone cannot achieve. After the authors addressed my main technical concerns in their rebuttal, I would like to recommend acceptance.", "rebuttal": "Thank you for your detailed comments and suggestions on our paper. Your feedback has been invaluable in helping us improve and refine our research. We greatly appreciate your efforts and have provided our responses to each of your points below:\n\n\n\n> **Q1: Feature Consistency Loss Concern** \n\nThank you for the insightful comment. Our feature consistency loss is designed to learn a shared feature space that remains effective across both past and new tasks. Simply freezing the entire multimodal encoder may preserve old knowledge **but limits the model’s adaptability**, especially when the original feature space does not sufficiently capture new task-specific semantics.\nInstead, we selectively fine-tune the modality-specific projectors within the encoder while applying consistency regularization to softly align the old and new feature spaces. **This design allows the representations to stay connected, yet flexible enough to adapt to new tasks.** It strikes a balance between preserving past knowledge and enabling plasticity for future learning.\nTo validate this design choice, we conducted an ablation study on the MP3D Continual ObjectNav dataset by freezing the entire multimodal encoder.\nThe results showed a performance drop across learning stages, confirming that full freezing harms generalization and underscoring the necessity of our consistency loss.\n\n| Methods   | Stage1       |        | Stage2       |        | Stage3        |         | Stage4        |         |\n|-----------|--------------|--------|--------------|--------|---------------|---------|---------------|---------|\n|           | SR           | SPL    | SR           | SPL    | SR            | SPL     | SR            | SPL     |\n| Frozen    | 39.88        | 13.32  | 34.64        | 9.72   | 33.35        | 9.44  | 32.8         | 9.32  |\n| Unfrozen  | **39.88**        | **13.32** | **38.88**        | **12.94**  | **38.21**         | **12.35**  | **36.63**         | **11.65**   |\n\n\n> **Q2: Adaptive Experience Feature Selection**\n\nThank you for your constructive feedback. To validate the effectiveness of our proposed Adaptive Experience Feature Selection mechanism, we conducted an ablation study on the MP3D Continual ObjectNav dataset with two sets of experiments:\n\n* Experiment 1: We compared our method against alternative experience selection strategies, including clustering-based sampling and uniform sampling. For a fair comparison, in the clustering-based approach, we set the number of cluster centers to half the trajectory length and selected frames closest to each cluster center in the feature space as key decision frames.\n\n* Experiment 2: We evaluated the robustness of our method when selecting fewer key frames, by reducing the selection ratio.\n\n\n| Methods   | Stage1       |        | Stage2       |        | Stage3        |         | Stage4        |         |\n|-----------|--------------|--------|--------------|--------|---------------|---------|---------------|---------|\n|           | SR           | SPL    | SR           | SPL | SR           | SPL   | SR            | SPL    |\n|  Clustering-based   | 39.88        | 13.32  | 35.32        | 11.47  | 31.04         | 10.17   | 33.39         | 10.84   |\n| Uniform  | 39.88        | 13.32  | 34.59        | 11.34  | 34.33         | 11.31   | 31.94         | 9.67    |\n| C-Nav     | **39.88**        | **13.32**  | **38.88**        | **12.94**  | **38.21**         | **12.35**   | **36.63**         | **11.65**   |\n\nResults from Experiment 1 demonstrate that our method outperforms the alternatives. From a data distribution perspective, frames that exhibit significant semantic shifts—**such as entering new spaces, approaching target objects, or reaching decision points—are typically rare and can be viewed as outliers in the feature space**. Our method explicitly aims to identify and preserve such outlier frames, which are more informative for navigation decisions. In contrast, **clustering-based or uniform sampling methods tend to select more “normal” frames** that lack critical decision relevance, leading to suboptimal performance.\n\n| Methods   | Stage1       |        | Stage2       |        | Stage3        |         | Stage4        |         |\n|---------|--------------|--------|--------------|--------|---------------|---------|---------------|---------|\n|         | SR           | SPL    | SR           | SPL    | SR            | SPL     | SR            | SPL     |\n| 0.1     | 39.88        | 13.32  | 33.45        | 11.53  | 30.8          | 9.72    | 34.17         | 10.84   |\n| 0.3     | 39.88        | 13.32  | 37.9         | 12.14  | 37.47         | 12.07   | 36.17         | 11.61   |\n| 0.5     | 39.88        | 13.32  | **38.88**        | **12.94**  | **38.21**         | **12.35**  | 36.63         | 11.65   |\n| 1       | 39.88        | 13.32  | 38.37        | 12.29  | 37.32         | 10.97   | **36.81**         | **11.86**   |\n\nResults from Experiment 2 show that our method maintains strong performance even when selecting a smaller proportion of key frames. Notably, **our approach achieves comparable or better results while using only 1/5 of the memory compared to clustering-based or uniform sampling baselines**, highlighting its efficiency and scalability.\n\nThank you very much for your valuable suggestions, which have been incredibly helpful in improving our work. We will incorporate the above results into the revised version of the paper to further demonstrate the effectiveness of the proposed method. We hope our response addresses your concerns. If you have any additional questions or feedback, we sincerely welcome them and will be glad to provide further clarification. Thank you again for your time and thoughtful review."}, {"review_id": "TRJzlY2wUb", "reviewer": "Reviewer_3LhB", "summary": "This 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", "questions": "- 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?", "limitations": "- 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.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### 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.", "was_revised": true, "final_justification": "After receiving the criticism, I was able to understand the C-Nav model. The author may need to update the writing.\nI recommend boderline accept.", "rebuttal": "We greatly appreciate your insightful comments and suggestions, as they have been helpful in refining and enhancing our work. We have thoroughly reviewed all of your points and have addressed your concerns as outlined below: \n> **Q1: Clarify Method with More Diagrams and Intuition.**\n\nThank you very much for your feedback. We have revised both the Introduction and Figure 1 accordingly.\n\n- Figure 1 has been updated with **a new subfigure (b)** to visually demonstrate how the agent loses its ability to navigate to previously learned goals after learning new tasks, thus illustrating the challenge of catastrophic forgetting in continual object navigation.\n- We revised the **figure caption** to clarify the key concepts. The new caption reads: Continual Object Navigation: The agent must continually learn from new data while retaining its ability to navigate to previously seen object goals. Subfigure (b) shows representative trajectories from a baseline model and C-Nav when evaluated on an old task after learning a new one.\n- Additionally, we have added **an explanation of Figure 1** in the Introduction, emphasizing the underlying challenges of continual learning. Specifically:\nAs the agent is exposed to new tasks over time, distributional shifts in sensory inputs and action patterns introduce biases into the model components, leading to representation drift and policy degradation. This results in catastrophic forgetting of previously acquired knowledge, **as illustrated in Figure 1**. The agent often becomes capable of semantic exploration only for objects in the current task, losing its ability to navigate to goals learned earlier.\n\n\n**The algorithm pseudocode and trajectory visualizations in the supplementary material (already included in the original submission)** provide a more detailed and intuitive understanding of our method.\n\nThese updates make the motivation and challenges of continual object navigation more visually accessible and help contextualize our proposed solution early in the paper.\n\n\n> **Q2: Add Visualizations or Qualitative Examples**\n\nThank you for the suggestion. We provide qualitative trajectory analysis in **Section C.3 (Case Study) of the supplementary material (already included in the original submission)**, including:\n- **Four TopDown visualizations** (Figure 7 & 8), illustrating agent behavior across stages;\n- **Four video clips**, showing first-person navigation trajectories of both the baseline model and C-Nav when evaluated on old tasks after multi-stage training.\n\nDuring evaluation, we focused on two episodes: locating a **toilet (stage 1)** and **a bed (stage 2)**. The visualizations in Figure 7 and Figure 8 clearly show that the **baseline agent exhibits catastrophic forgetting**. After multi-stage training, it consistently navigates toward the sofa (the target in the final stage), completely forgetting the navigation strategy for older objects. In contrast, the C-Nav model **successfully navigates to both the toilet and bed targets**, demonstrating superior long-term task retention.\n\nTo provide a more intuitive comparison, we plan to **move these trajectory visualizations to the main experimental results section** in future versions of the paper. This will better highlight the challenges in continual object navigation and allow a clearer comparison of model performance.\n> **Q3: Include More Recent Rehearsal Baselines**\n\nThank you very much for your valuable feedback. In our experimental section, we have compared our method against several representative continual learning baselines, including Data Replay, LoRA, LwF, and Model Merge. Among them, Data Replay serves **as a strong baseline** due to its effectiveness in mitigating catastrophic forgetting.\nOur results demonstrate that the proposed C-Nav outperforms these methods not only in terms of navigation performance, but also **offers clear advantages in storage efficiency and data privacy**, thanks to our adaptive experience selection and dual-path forgetting mitigation strategy.\n\n\nWe have also added a comparison with **ZSCL**[1], a recent method that imposes constraints in both parameter and feature space to support continual learning. The results are summarized below:\n\n|Methods(HM3D)|Stage1| |Stage2| |Stage3| |Stage4| |\n|---|---|---|---|---|---|---|---|---|\n| |SR|SPL|SR|SPL|SR|SPL|SR|SPL|\n|Finetuning|69.63|31.25|23.38|9.14|13.24|3.57|19.5|7.59|\n|Lora|69.63|31.25|30.6|11.85|13.36|4.95|22.5|9.58|\n|LWF|69.63|31.25|27.48|9.5|10.53|2.55|19.2|6.94|\n|MAE|69.63|31.25|51.23|22|35.22|12.99|24.5|7.19|\n|Replay|69.63|31.25|56.14|22.91|45.5|17.88|39.6|16.48|\n|**ZSCL**|69.63|31.25|31.72|11.3|25.55|9.21|24.6|8.84|\n|Ours|69.63|31.25|**59.34**|**26.35**|**47.78**|**21.26**|**46.5**|**20.22**|\n\nAlthough ZSCL performs reasonably well in earlier stages, its performance drops considerably in later stages. For example, in Stage 4, **its SR drops to 24.6 compared to 46.5 achieved by C-Nav**.\nThis highlights a key limitation of existing continual learning frameworks, which are often designed for **static, perception-focused tasks like image classification or image-text matching**.\nThese tasks do not involve **long-horizon exploration, target identification, active perception, or obstacle avoidance**, which are essential components of continual object navigation. \nAs a result, such methods are difficult to directly apply to this setting.\nIn contrast, our task explicitly incorporates these embodied challenges, and **C-Nav is designed to handle these embodied challenges, enabling effective continual learning in complex, dynamic settings**.\n\nWe are also extending our comparisons to include additional recent methods, and the results will be reported in the final version of the paper.\n\nWe hope this response sufficiently addresses your concerns.\n\n\n[1]Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models\n\n> **Q4: Evaluate Category Identity Preservation Across Stages**\n\nYes, we have examined how the model handles repeated object categories that appear in different stages. **As discussed in Section C.3 of the supplementary material**, we selected a model that had been trained across all four stages and visualized its navigation trajectories in tasks from **Stage 1 (toilet) and Stage 2 (bed)**. This analysis helps reveal how well the model retains knowledge of earlier tasks after continual fine-tuning.\nOur TopDown visualizations (Figures 7 and 8) and first-person video comparisons show that the finetuning baseline exhibits clear signs of forgetting, often navigating toward the most recently trained object goal. In contrast, **C-Nav retains prior knowledge and successfully navigates to object goals from earlier stages**, demonstrating greater stability and resistance to forgetting throughout the continual learning process.\n\nTo provide quantitative evidence, we further evaluated each model on old tasks across all stages, as shown in the following table:\n\nPerformance on Old Tasks at Each Stage (HM3D): \n|Methods|Stage1| |Stage2| |Stage3| |Stage4| |\n|---|---|---|---|---|---|---|---|---|\n| |SR|SPL|SR|SPL|SR|SPL|SR|SPL|\n|Finetuning|69.63|31.25|7.72|2.68|9.83|2.16|9.05|2.55|\n|LoRA|69.63|31.25|17.99|6.27|8.19|2.59|11.70|4.39|\n|Lwf|69.63|31.25|16.19|4.58|8.64|1.74|10.22|2.92|\n|Merge|69.63|31.25|66.03|28.93|41.62|15.40|25.12|7.46|\n|Data Replay|69.63|31.25|54.60|22.57|48.92|19.73|32.94|13.71|\n|C-Nav|69.63|31.25|**61.38**|**26.93**|**52.27**|**22.90**|**42.61**|**17.52**|\n\nThese results quantitatively support the superior forgetting resistance of C-Nav compared to all tested baselines.\n\n\n> **Q5: Lack of qualitative analysis and visualization**\n\nAs mentioned in Answer1 and Answer2, we have incorporated qualitative visualizations and video comparisons to highlight what the model is learning and how forgetting manifests in practice. Specifically:\n- Figure 7 and Figure 8 in the supplementary materials provide **TopDown trajectory visualizations,** contrasting the finetuning baseline and our C-Nav model.\n- **Four accompanying video clips** show the first-person navigation behavior of both models in various environments, targeting previously seen objects.\n- These comparisons further highlight C-Nav's ability to preserve semantic understanding across tasks, even after multi-stage training.\n  \nWe hope these additions help clarify our approach and provide a more comprehensive understanding of the model's capabilities\n\n> **Q6: Improve figure explanation**\n\nAs also addressed in Answer1 and Answer2, we have **revised Figure 1**, the **Introduction**, and the **case study in the Experimental Results section** to improve clarity and better highlight the challenges of continual object navigation.\n\n> **Q7: Analyze stage-order sensitivity**\n\nWe appreciate the reviewer’s insightful suggestion regarding task order sensitivity. To assess the robustness of our method to stage-order permutations, we conducted ablation studies with three different task orderings. The object categories were regrouped across stages.  \n\n\nWe summarize SR and SPL across all stages in the following table(MP3D):\n\n|Methods|Stage1| |Stage2| |Stage3| |Stage4| |\n|---|---|---|---|---|---|---|---|---|\n| |SR|SPL|SR|SPL|SR|SPL|SR|SPL|\n|Order1|39.88|13.32|37.44|10.02|36.19|9.7|37.22|10.1|\n|Order2|39.88|13.32|35.7|10.51|33.59|9.36|32.12|8.17|\n|Order3|39.88|13.32|39.4|13.32|36.03|8.93|33.17|8.66|\n\nThese findings suggest that C-Nav is **robust to different stage sequences**, making it well-suited for real-world continual learning scenarios with dynamic task orders.\n\n\nThank you again for your valuable suggestions. We will incorporate the relevant additions and improvements in the final version. We hope our response adequately addresses your concerns and would greatly appreciate your consideration for a higher score. Should you have any further questions, we are happy to respond."}, {"review_id": "kKZoW79s4u", "reviewer": "Reviewer_23E4", "summary": "This 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.", "questions": "- 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?", "limitations": "The author discussed the limitations on page 9.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "## 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.", "was_revised": true, "final_justification": "After rebuttal, I believe some of my concerns remain unaddressed.", "rebuttal": "We greatly appreciate your insightful comments and suggestions, as they have been helpful in refining and enhancing our work. We have thoroughly reviewed all of your points and have addressed your concerns as outlined below: \n\n\n> **Q1: Clarification on the Need for Continual Learning with Limited Object Categories**\n\nThank you for raising this important question. While HM3D contains only 6 categories, we also evaluate on MP3D, which offers **21 diverse and semantically rich categories** (e.g., gym_equipment, fireplace), providing a more realistic benchmark.\n\nOur goal is not merely to increase category diversity, but to **address fundamental challenges in continual learning for embodied object navigation**—a setting largely overlooked in prior work. Existing methods are primarily designed for static perception tasks like image classification or retrieval. In contrast, our task involves **long-horizon exploration, multimodal input, obstacle avoidance, and active perception**, introducing unique and complex forgetting patterns.\n\nEven under controlled staged settings, we observe **catastrophic forgetting, semantic confusion, and task interference**, which significantly degrade navigation performance and are **not effectively addressed by current methods**.\n\nOur framework offers a scalable and diagnosable platform to study these challenges. We hope this work provides valuable insights toward building embodied agents that are adaptive, efficient, and capable of long-term deployment in dynamic environments.\n> **Q2: Ablation Studies**\n\nIn response, we now provide detailed ablation studies on key hyperparameters.\n\n(1) Loss Weighting Coefficients (MP3D)\n\n\n|λ|Stage1| |Stage2| |Stage3| |Stage4| |\n|-|-|-|-|-|-|-|-|-|\n| |SR|SPL|SR|SPL|SR|SPL|SR|SPL|\n|1|39.88|13.32|38|12.25|35.41|**11**|36.22|10.85|\n|5|39.88|13.32|**38.37**|**12.29**|**37.32**|10.97|**36.81**|**11.9**|\n|10|39.88|13.32|36.66|11.8|36.68|11.45|25.29|9.51|\n\nAs shown in the table,  we chose λ = 5 as the optimal weight for both losses, **balancing the retention of old knowledge and the ability to learn new tasks effectively.**\n\n\n(2) LOF Frame Retention Ratio (MP3D)\n\n|Retention Ratio|Stage1| |Stage2| |Stage3| |Stage4| |\n|-|-|-|-|-|-|-|-|-|\n| |SR|SPL|SR|SPL|SR|SPL|SR|SPL|\n|0.1|39.88|13.32|33.45|11.53|30.8|9.72|34.17|10.84|\n|0.3|39.88|13.32|37.9|12.14|37.47|12.07|36.17|11.61|\n|0.5|39.88|13.32|**38.88**|**12.94**|**38.21**|**12.35**|36.63|11.65|\n|1|39.88|13.32|38.37|12.29|37.32|10.97|**36.81**|**11.86**|\n\nAs shown in the table, the model performance decreases as the retention ratio drops. However, the overall decline is relatively mild. Notably, **even when retaining only 10%** of the key frames, our method still demonstrates competitive performance, highlighting the robustness of our approach in selecting relevant experiences for continual learning.\n> **Q3: Include More Recent Continual Learning Methods**\n\nThank you very much for your valuable feedback. In our experiments, we compared C-Nav with several representative continual learning baselines, including Data Replay, LoRA, LwF, and Model Merge. Among these, Data Replay serves **as a strong baseline** due to its ability to mitigate catastrophic forgetting.\n\nOur results demonstrate that the proposed C-Nav outperforms these methods not only in terms of navigation performance, but also **offers clear advantages in storage efficiency and data privacy**, thanks to our adaptive experience selection and dual-path forgetting mitigation strategy.\n\nWe have also added a comparison with **ZSCL**[1], a recent method that imposes constraints in both parameter and feature space to support continual learning. The results are summarized below:\n\n|Methods(HM3D)|Stage1|Stage2|Stage3|Stage4|\n|---|---|---|---|---|\n| |SR|SR|SR|SR|\n|Finetuning|69.63|23.38|13.24|19.5|\n|Lora|69.63|30.6|13.36|22.5|\n|LWF|69.63|27.48|10.53|19.2|\n|MAE|69.63|51.23|35.22|24.5|\n|Data Replay|69.63|56.14|45.5|39.6|\n|**ZSCL**|69.63|31.72|25.55|24.6|\n|Ours|69.63|**59.34**|**47.78**|**46.5**|\n\nWhile ZSCL performs reasonably well in early stages, its performance declines significantly in later stages. For example, in Stage 4, its SR **drops to 24.6, compared to 46.5 achieved by C-Nav**. This reflects a broader limitation of many continual learning methods that are primarily developed for **static, perception-focused tasks such as image classification or image-text matchin**g. These tasks do not involve **long-horizon exploration, active perception, or obstacle avoidance**, which are essential for embodied navigation.\n\nIn contrast, our task setting integrates these embodied challenges, and C-Nav is specifically designed to address them, enabling more effective continual learning in complex, dynamic environments.\n\nWe are further expanding our comparisons to include additional recent methods, and the updated results will be presented in the final version of the paper.\n\nWe hope this response sufficiently addresses your concerns.\n\n[1]Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models\n> **Q4: Include Qualitative Trajectory Analysis**\n\nThank you for the suggestion. We provide qualitative trajectory analysis in **Section C.3 (Case Study) of the supplementary material**, including:\n- **Four TopDown visualizations** (Figure 7 & 8), illustrating agent behavior across stages;\n- **Four video clips**, showing first-person navigation trajectories of both the baseline model and C-Nav when evaluated on old tasks after multi-stage training.\n\nDuring evaluation, we focused on two episodes: locating a **toilet (stage 1)** and **a bed (stage 2)**. The visualizations in Figure 7 and Figure 8 clearly show that the **baseline agent exhibits catastrophic forgetting**. After multi-stage training, it consistently navigates toward the sofa (the target in the final stage), completely forgetting the navigation strategy for older objects. In contrast, the C-Nav model **successfully navigates to both the toilet and bed targets**, demonstrating superior long-term task retention.\nTo provide a more intuitive comparison, we plan to **move these trajectory visualizations to the main experimental results section** in future versions of the paper. This will better highlight the challenges in continual object navigation and allow a clearer comparison of model performance.\n> **Q5: Report Old vs. New Task Performance per Stage**\n\nFollowing your suggestion, we report the results on the HM3D dataset by separately evaluating performance on old and new tasks.\n\nNew Task Performance:\n|Methods|Stage1|Stage2|Stage3|Stage4|\n|-|-|-|-|-|\n|Finetuning|69.63|60.55|29.54|64.63|\n|LoRA|69.63|60.55|38.08|69.15|\n|Lwf|69.63|54.27|19.57|57.98|\n|Merge|69.63|16.08|4.63|21.81|\n|Data Replay|69.63|59.80|29.18|68.35|\n|C-Nav|69.63|54.52|26.33|63.30|\n\nOld Task Performance:\n|Methods|Stage1|Stage2|Stage3|Stage4|\n|-|-|-|-|-|\n|Finetuning|69.63|7.72|9.83|9.05|\n|LoRA|69.63|17.99|8.19|11.70|\n|Lwf|69.63|16.19|8.64|10.22|\n|Merge|69.63|66.03|41.62|25.12|\n|Data Replay|69.63|54.60|48.92|32.94|\n|C-Nav|**69.63**|**61.38**|**52.27**|**42.61**|\n\nThese results clearly demonstrate that:\n- **Finetuning** suffers from severe **catastrophic forgetting**, with old-task performance dropping drastically as new tasks are introduced.\n- **C-Nav** maintains stable performance across both old and new tasks, effectively balancing stability and plasticity. In the final stage, it achieves a **10% higher success rate** on old tasks compared to the best-performing baseline.\n\nWe will include this breakdown in **Appendix C.4**, along with further analysis to highlight the trade-offs involved in continual object navigation.\n>**Q6: Justify L2 Feature Constraint for Representation Stability**\n\nWe adopt an L2-based feature distillation loss to enforce consistency in the encoder's representations across stages. This simple yet effective constraint mitigates representation drift. \nThe design is inspired by **prior work on feature-level knowledge distillation** [2], where **L2 regularization helps preserve essential semantic structures from earlier tasks**. Ablation results (Table 4) show that removing this constraint leads to substantial performance degradation (**−22.15 SR on HM3D and −15.92 on MP3D**). This confirms that stabilizing the encoder’s feature space is crucial for long-term retention, and supports the effectiveness of our L2 constraint in mitigating forgetting.\n\n[2] CVPR 2019：Relational knowledge distillation.\n>  **Q7: C-Nav Shows Performance Decline in Stage 4 While Several Baselines Recover**\n\nAs shown in **Answer 2**, all baseline methods experience significant performance drops on old tasks, indicating **severe forgetting**. In contrast, C-Nav effectively mitigates this, **achieving a 10% higher success rate** on old tasks than the best baseline in the final stage.\n\nThe recovery in some baselines during Stage 4 is likely due to two factors:\n- **Task Difficulty**: Stage 4 introduces \"sofa\" as the target, which is easier than \"TV\" in Stage 3. The base model's new-task success rate jumps from 29.54% in Stage 3 to 64.63% in Stage 4, suggesting **overfitting to the easier task, rather than true continual learning**.\n- **Semantic Similarity**: The target \"sofa\" is visually similar to previously learned objects like \"bed\" and \"chair,\" leading to potential confusion and misidentification, which could **falsely improve performance on old tasks**.\n\nIn contrast, C-Nav prioritizes consistent performance across stages, focusing on **long-term knowledge retention** over short-term gains. This slight decline in Stage 4 reflects its cautious adaptation strategy, designed to prevent overfitting and ensure stability across extended task sequences.\n\n\nThank you for your valuable feedback. We hope our responses address your concerns and are happy to clarify further if needed."}]

Author rebuttal:

Meta-review: This paper introduces a benchmark for continual learning in object navigation. To make progress on this benchmark, they introduce a new method called C-NAV.

Reviewers saw the strength in this work -- the task paradigm itself is valuable, the proposed method is interesting, and the evaluations are thorough. While there were initially some concerns about the clarity of the presentation and novelty of the method, these were adequately addressed in the rebuttal.
Decision: Accept (poster)