All-day Multi-scenes Lifelong Vision-and-Language Navigation with Tucker Adaptation
Authors: Xudong Wang
Gan Li
Zhiyu Liu
Yao Wang
Lianqing Liu
Zhi Han
Venue: ICLR 2026 Poster (2026)
Keywords: Tensor Decomposition
Vision-and-Language Navigation
Lifelong Learning
Primary area: applications to robotics, autonomy, planning
TL;DR: We propose Tucker Adaptation (TuKA) for VLN agents lifelong learning with multi-hierarchical knowledge in a high-order tensor, achieving all-day multi-scenes lifelong VLN.

Abstract: Deploying vision-and-language navigation (VLN) agents requires adaptation across diverse scenes and environments, but fine-tuning on a specific scenario often causes catastrophic forgetting in others, which severely limits flexible long-term deployment. We formalize this challenge as the all-day multi-scenes lifelong VLN (AML-VLN) problem. Existing parameter-efficient adapters (e.g., LoRA and its variants) are limited by their two-dimensional matrix form, which fails to capture the multi-hierarchical navigation knowledge spanning multiple scenes and environments. To address this, we propose Tucker Adaptation (TuKA), which represents the multi-hierarchical navigation knowledge as a high-order tensor and leverages Tucker decomposition to decouple the knowledge into shared subspaces and scenario-specific experts. We further introduce a decoupled knowledge incremental learning strategy to consolidate shared subspaces while constraining specific experts for decoupled lifelong learning. Building on TuKA, we also develop a VLN agent named AlldayWalker, which continually learns across multiple navigation scenarios, achieving all-day multi-scenes navigation. Extensive experiments show that AlldayWalker consistently outperforms state-of-the-art baselines.

Published as a conference paper at ICLR 2026
ALL-DAY MULTI-SCENES LIFELONG VISION-AND- LANGUAGE NAVIGATION WITH TUCKER ADAPTATION
Xudong Wang*...3 Gan Li Zhiyu Liu Yao Wang", Lianqing Liu', Zhi Han
State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences,
2 North University of China,
3 University of Chinese Academy of Sciences,
4 Xi an Jiaotong University
Low-Rank Adaptation (LoRA)
McE-LoRd LORA (HydraLoRA)
Tucker Adaptation (TuKA)
Single Task wise Specific Knowledge Learning
Single knowledge Shared and Specific Leeming
Mutt-Hicrarchie Knowledge Decoupling Learning
Lifeling
Single
Specthic
Malric Adopters
Litebing
Speciss
Malticas
Mull- Lifiing
Adaptors
Tenser Adapler
Scone
Scere
Scome
Scane
Scone
Soono
Sceres
light
Knowledge2 Environments
AlldayWalker
Scenss
Oveneooauure
DeCrOEDOUUPC
Narmal
Scalbering
Scene with mull-enviromments
Scene P with mull-enviromments
Sc Scaltaring
Scene q with muliieniviromments
Task
Task 1-1
Task I...
Illustration of the proposed all-day multi-scenes lifelong lision-and-language navigation learning and Tucker Adaptation (TuKA). It requires VIN agents continually learn across multiple scenes and diverse environments (low-light, overexposure, and scattering), progressively consolidat- ing navigation knowledge to achieve all-day multi-scenes navigation. Different from LoRA and its variants, which only perform continual learning with single-dimensional task knowledge, our pro- posed TuKA decouples and represents multi-hierarcha task knowledge high-order tensor.
Figure 1:
ABSTRACT
Deploying assion-add-lanuuall navigation (VLN) agents requires adaptation across diverse scenes and environments, but fine-tuning on a specific scenario often causes catastrophic forgetting in others, which severely limits flexible long- term deployment. We formalize this challenge the all-day multi-scenes lifelong VLN (AML-VLN) problem. Existing sarameter-efffcieee adapters (c.g., LoRA and its variants) are limited by their two-dimensional matrix form, which fails to capture the multi-hierarchical navigation knowledge spanning multiple scenes and environments. To address this, we propose Tucker Adaptation (TuKA), which represents the multi-hierarche navigation knowledge high-order tensor and leverages Tucker decomposition to decouple the knowledge into shared subspaces and -peeefi--peecficc experts. We further introduce decoupled knowledge incre- mental learning strategy to consolidate shared subspaces while constraining spe- cific experts for decoupled lifelong learning. Building TuKA, also develop VI.N agent named A Allday Walker, which continually learns across multiple naviga- tion scenarios, achieving all-day multi-scenes navigation. Extensive experiments show that Allday Walker consistently outperforms state-of-the-art basclines. Code and video demos are available at: http://aannii-iiiiiiie http://aanii....ggthh.oo/////l/a Walker/
INTRODUCTION
Vision-and -ontn-and-Langunnn Language Navigation (VLN) requires embodied agents to follow user instructions and reach target locations a navigation scene (Lin et al, (2025); Wei et al. (2025): Zhang et al. (2025c)).
Equal contribution. Corresponding author.

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Published as a conference paper at ICLR 2026
Since its introduction (Anderson et al. (2018) VLN research has rapidly advanced from early stud-
ies in discrete, graph-baser simulators (Anderson et al. (2018): al (2020): Wang et al. (2026a)
to continuous embodied platforms (Krantz et al. (2020)) large-scale persistent navigation bench-
marks (Krantz et al. (2023): [Song et al. (2025) ). and even real deployments (Wei et al. 2025
/Zhag al. 2024a)) These advances make VIN capability for robots that interact with humans.
However, similar to many other robotics learning tasks (Xiao et al. (025); Wang et al. (023d);
Liang et al. (2025). ). directly deploying VLN agents in the real world often falls short of practical requirements. To achieve reliable performance, agents typically require fine-tuning to adapt to a specific scenario. Yet in real applications, agents often operate in dynamic scenarios that involve both diverse scenes and illumination environments. Adapting to one specific scenario usually comes at the cost of degraded performance in others, leading to catastrophic forgetting (Meng et al. (2025); Yao et al. (2025): /huu et al. 22255;; Ayub Ayyb et al.) (205)). Inspired by human lifelong learning and continual evolution, we aim to build VLN agents that can learn across multiple scenes and environments, evolving over time to achieve all-day multi-scenes navigation without forgetting, as shown Figure
Parameter-efficient adapters such as LoRA (Hu et al. (2022) are widely used to adapt pretrained large models with only few extra parameters. However, applying separate LoRA modules per nav- igation scenario fails to capture cross-task shared knowledge, preventing the agents from leveraging accumulated knowledge to improve adaptation to new scenarios. To explore task-shared knowl- edge, mixture-of-expert LoRA variants (e.g., Hydral (Tian et al. (2024)) Dense MoLEE (Chen) let al. (2024), BranchLoRA (Zhang et al. (2025a)) employ multi-expert co-activation represent shared components. As illustrated in the upper part of Figure | these LoRA-based approaches still represent knowledge using two hierarchical matrices (one shared factor with several task-specific matrices). Such a two-dimensional, matrix- based representation is inherently limited for learning multi-hierarchiie. navigation knowledge that spans multiple scenes and diverse environments. In- spired by the powerful capability in high-dimensionas space representation learning (Verleysen et al. 20033; : (Stöcki et al. (, we ask: can we represent the multi- hiltrrhierrrcliaal knowledge in high- order tensor, thereby enabling stronger multi-hiirarchical, decoupled representation learning?
To realize high-dimensional space representation learning with a high-order tensor, we identify two critical challenges: i) how to continually decouple representation learning across nulti-hierarchicl knowledge, and ii) how to align the higher-order dimensions with the two-dimensional matrix back- bone used in LLM adaptation. To address the challenges, we propose Tucker Adaptation (TuKA), a new fine-tuning method that employs Tucker decomposition (Kolda & Bader (2009)) to repre- navigation knowledge. TuKA represents scene knowledge and environmen-
sent multi-hierarchicn tal knowledge in distinct expert factor matrices, and represents shared knowledge across multiple tasks using shared core tensor and encoder-decoder. To align LLM parameters, TuKA selects each specific expert from row within its entire expert factor matrices, reducing the expert matrix dimen- sion to a vector. Thus, the higher-order knowledge tensor is reduced two-dimensional weight matrix for aligning LLM parameters. We further design Decoupled Knowledge Incremental Learn- ing (DKIL) strategy that consolidates shared subspaces while constraining the task -pee-fie experts to mitigate catastrophic forgetting, during multi-hierarchical knowledge lifelong learning. Build- ing on TuKA, we also develop Allday Walker, a lifelong VLN agent that continually adapts across multiple scenes and diverse environments. To support this study, we extend an existing embodied navigation simulator with multiple degraded imaging models (including low-light, scattering, and overexposure) to produce diverse environments for training and evaluation. Extensive experiments are performed to demonstrate that Allday Walker outperforms the SOTA fine-tuning methods in life- long VLN performance, enabling all-day multi-scenes VLN. Our contributions are threefold:
We formalize the all-day multi-scenes lifelong VLN learning problem, and propose a novel parameter-efficient adaptation method named TuKA decouple and represent the multi- hierarchical knowledge in a high-order tensor, for more powerful representation learning.
We develop Allday Walker, an all-day multi-scenes lifelong VLN agent that continually evolves using a decoupled knowledge incremental learning strategy across multiple navi- gation scenes and diverse environments, thus achieving all-day multi-scenes navigation.
We extend the existing embodied navigation simulators with imaging models to construct an all-day multi-scenes lifelong VLN benchmark for evaluation with diverse environments. And additional
deployments also validate the superiority of our Walker.
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2 PRELIMINARY AND PROBLEM FORMULATION
navigation (VLN) task, an embodied agent is required to understand user language instruction I, e.g., "walk forward to the white wooden table, turn right into the bedroom, turn left to the wardrobe", and follow the instruction to navigate in scene S. Follow- ing prior work on VLN agents (Wei et al. 2025); Zheng et al. (2024); Zhang et al. (2025b); Gao et al.) (025a)). ) we adopt pre-trained large language model (LLM). Qwen2-7B 7B (Team (024)), with to- kenizer e, as the backbonn agent F. For encoding the agent's s video stream observations O we use the CLIPP vision encoder V(O), consistent with Stream VLN (Wei et al. (2025)). The overall architec- ture is similar to multimodal large language models such as LL Video (Zhang et al. (2024c). ). At each navigation step i, the agent F reasons the user instruction I and the current observation O' to generate the next action: It = (e)(O"),((I')) E A, where the action space A consists of four low-level navigation actions: A (FORWARD (0.25m), TURN LEFT (159), TURN RIGHT (15%), ) supporting continuous navigation in embodied environments as in VLN-CE (Krantz et al. (2020)) ) In real-worlk dynamic deployments. however, agents inevitably face diverse scenes and en- vironments (e.g.. low-light, overexposure, scattering), which severely degrade performance (Yang) ct al. 2025a); Solmaz et al. (2024)). As illustrated in Figure 2 adapting to a new navigation sce- nario St (defined by a specific scene S. and environment F.e) often causes catastrophic forgetting of previously learned scenarios {S1, S2, ..., S,-1). thus limiting flexible practical deployment.
Preliminary: In the vision-and-langee
To tackle the above challenge, we introduce a new problem setting, all-day multi-scenes lifelong daston-and-languagee navigation (AML-VLN). In this setting, the agent is required to continually learn a
Problem Definition:
85
Success Rate (9%)
600
sequence of navigation scenarios while alleviating the
forgetting of old navigation scenarios, thereby forming an all-day, multi-scenes universal VLN agent. The VLN Multi- hietr-hhirar Knowledge of each scenario T; in- cludes a specific scene S. under a specific environ- ment E.. Formally, let T {Ti T2..2..... T2.... T.} denote a sequence of navigation scenarios. Each scenario T; is defined by pair (S, E). where the scene set S (S1, S.,..., Sar) includes M scenes and the environment set 3 = [,,, E2, ..., EN} includes N environments. The VLN agent JF must learn all tasks T sequentially, and evaluation is conducted across all scenarios after train- ing. Importantly, in AML-VLY the task-id t is seen during agent training but is agnostic during the testing phase, and each new scenario {S,, E,} does not overlap with any previous scenario: (St, F.e) nU; F2 = 0. A trivial solution is to store all adaptation weights for all past tasks and load them during inference. However, navigation tasks inherently share common and task- specific knowledge: for example, the same scene under different environments (day vs. night), or different scenes under the same environment. Thus, the crucial challenge of AML-VLN is to explore and exploit shared and specific knowledge across multiple scenarios for efficient lifelong learning.
10
Step tine Sequumiia Fine-tre
Forgetting
Figure 2: Illustration of catastrophic for- getting in lifelong navigation learning. The new scenario adaptation leads to catastrophic forgetting of old scenarios.
METHOD
In this section, we first analyze the limitations of the existing low-rank adapters, including vanilla LoRA and MoE LoRA family models ($3.1). Then, we introduce Tucker-Adaption (TuKA) archi- tecture (.... describe how to perform continual learning with TuKA ($3.) and inference ($3.4)
3.1 EXISTING LOW-RANK ADAPTION
Low-Rank Adaptation (LoRA) (Hu et al. (2022) enables efficient fine-tuning of large language models by injecting low-rank adaptation weights into each transformer layer. Specifically, for the I-th layer with backbone weights W LoRA introduces update A AW' = BB' A', where B' E R is a low-rank pisenssio--riiinng matrix and A' E x is a rankrank cemennioon-rdduciin shown in Figure 3(a), the layer output is computed as y W +B, This method lears each task-spe knowledge, making suitable for specific single-task continual learning. However, its task-specific independent structure limits the ability to explore and reuse shared knowledge across
matrix. As
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LoRA: Specific Knowledge
HydraLoRA: Shared and Specific [Muit-Matter Matrices Adapter)
TuKA: High-dimensional Space Knowledge Representation
[Malrix Adapler)
(High-Order Tensor Adapter)
Inputs
Incule
Tensor Tucker Decompos
LUM
A
Fronde
X
Altertiin
FFN
FRN
Tensor Marti A lignment
(lartion)
Attenien
Kains
A.V
Speciilc Knsw udge
Quppars
Outputs
Sharee Know edlee
Ourpuss
(a) Vanilla LoRA
(b) HydraLoRA
(c) Our Tucker Adaptation (TuKA)
Figure 3: Illustration for comparison of existing LoRAs and our TuKA architecture. Different from the LoRA MoE-LoRA variants, which represent simple knowledge within wvo-hierarchical matrix, TuKA decoupling represents the multi-hierarchical knowledge within high-order tensor.
multiple tasks. To address this, Mixture-of offttrr--o--xpprrts (MoE) based methods (Zhang al. (2025a); Tian
et al. ) Gao et al.] (2024); Chen et al. (2024) extend LoRA by introducing MoE structures.
For example, Figure (b). HydraLoRA (Tian et al. (2024) proposes multiple tasks share single dimension-reducing matrix with multiple specific dimension-raising matrices {B,,... 3x):
K
W +AW W! x Σ A''
This design implicitly separates task-shared and task-specific woo-ierarchical navigation knowl- edge. But in AML-VLN setting, knowledge spans multiple hierarchical levels: core navigation skills, scene-specific knowledge, and environment-specifee knowledge. These methods represent all knowledge within hierarchical matrices: one shared matrix and several task-specific matrices. restricts them to representing only two hierarchical knowledge structures. This limitation motivates us to explore higher-order representations that can explicitly decouple malti-hierarchical knowledge.
3.2 TICKER-ADAPTIE ARCHITECTURE
To achieve nigh-dimensional space representation with a high-order tensor X E Rd i xde..... one of the critical challenges is how to align the higher-order dimensions with the two-dimensional matrix backbone LLM. few existing explorations have treated LLM backbones as tensors for learning (Jahromi & Orús 2024)), yet these methods only consider specific architectures, such as splicing multi-attention matrices into third-order tensors within multi-hean attention (Zhang et al.) 2025d)), or treating matrices as second-order tensors (Bershatsky et al. (2024)). These methods fundamentally fail to resolve the above dimensional alignment problem, and thus do not actually perform representation learning within high-order tensors. To address this, propose Tucker Adap- tation (TuKA), a new fine-tuning method that lifts adaptation into a high-dimensional tensor space. Formally, in order learn the 1-th navigation scenario task (with the s-th scene and the e-th vironment) T, = {Ss, }. we finetune the Stream VLN agent Fo, (Wei et al. (2025)) ). in a high- dimensional space, the task-specific data S, (O,,Ie), and then obtain updated Fe;- where 0. 0u + A0, A0, = (AW:) and AW! E xoi the updated weight in I-th layer of a total of L transformer layers. Specifically, in TuKA, we follow the tensor Tucker decomposition (Kolda Bader (2009) to decouple high-order tensor for multi-hierarchical knowledge decoupling rep- resentation and dimension alignment. Specifically, tensor X' R prixt M MxN can be decomposed:
x' 9 x1 U X2 2 X3 3 X U
where X,, 72 = 1,2,3, 4 denotes the n-th modal product of the tensor and matrix (Kolda & Bader 2009 G Xraxs is a core tensor, which contains interaction information between all patterns, and is used to learn the shared core navigation skills. Factor matrix UR represents the transformation pattern of the feature from ri dimension to a,, which can be regarded as shared decoder; U E Xra represents the transformation from by dimension r2, which can be regarded as a shared encoder. Factor matrix U E Mx is the M group of scene experts, with each scene expert + || is used represent the i-th specific scene knowledge; U e is N group of environment experts, with each expert U+ is used to represent the j-th specific environment knowledge. Thus, for the 1-th scenario with s-th scene and e-th environment adaptation, we can
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Lwe
1n Lewe
Low-light Environment
under Overaxposed Environment
Task
Low-light Environment
Shane Pipert
Endraiment Fapert
Scans Expert
Ersinoment Paper
Soan Expert
Eia Fow Pxpet
Enteder
Encadder
O
Dicooderr
Decocder
U4
U3 Tet
Shariic Mowrwwedyy
Shardd Kronllcge
Shared Krendanige
di
Scene 1
Low-ight
Sceme P
Ovenexposure
Scane
Low-ightt
Figure 4: Illustration of the proposed decoupled knowledge incremental learning. Our TuKA per- forms decoupled incremental learning for multi-hierarchical knowledge in high-dimensional space.
extract the task-specific 8 :] and U+ :] from tensor X' constitute adaptation weight AW; AW, = U' (g x3 [s. :] x1 :l): (U2)².
The TuKA represents a decoupled shared-specific architecture for the nulti-hierarchical knowledge (scene S, environment 8), in a eigh-dimensional space X' for effective adaptation, as Figure 3 (c).
3.3 DECOUPLED KNOWLEDDGE INCREMENTAL LEARNING
To realize decoupled representation learning for the multi-hierarchical knowledge within a higher- dimensional space X, we propose a Decoupled Knowledge Incremental Learning strategy (DKIL), as illustrated in Figure 4 Specifically, for the initial learning, we use the Kaiming initialization (He et al. 20155) initialize the factor matrices and core tensor {G, (=+)=²)) and U U with zero-initializationn and then we train only { (U") [1, U [1,:] to adapt scenario Ti.
Inheritance Scenario-Shared Knowledge. When learning subsequent new scenario task (with s-th scene and e-th environment) denoted as T, {S., E. >> continually, we perform expert knowl- edge inheritance the current core tensor G with the learned g', and the current encoder decoder {U1, U²} } with the learned {U' U²') J. For previous knowledge inheritance, we also initialize the current scene expert ($ :] or environment expert U+e,,:] with (s. or U U+(e, if previous scenario {=. has learned the same experts. This inheritance mechanism maintains the shared knowledge. In addition, to progressively refine the shared knowledge and avoid old knowledge catas- trophic forgetting, we also perform elastic weight consolidation on these shared subspaces:
Lewe,t= = =1 F
(l) (4)
where A1 is the balance hyper-parameter @ denoted as the Hadamard product, Fg. wxx.xxaaxaaaa..........xx... F Rexm, and F hxrrr are Fisher information weights (Kirkpatrick et al.. (017)) measuring the importance of each learnable parameter in T2-1, and can be calculated:
Fo.--1 E(s
(oo.,, logp()) 0
(5)
where (g, U.. _U3 0, and S I are the input data and y is the output navigation actions. It measures the sensitivity of each parameter O to the model's output probability, with higher value indicating greater importance. In addition, we also perform incremental updates to the t-th Fisher F; during the continual navigation learning gradually learn the shared knowledge:
Fo.: = w Fo.1-1 + (1 - w) Fo.::
where w is the exponential moving average coefficient to control the smooth update of Fisher Fe.--
In addition to the shared knowledge learning, including the core tensor and the encoder decoder {g, U U to avoid catastrophic forgetting of scene expert knowledge [3 :] and environment expert knowledge U :e. we also perform expert consistency constraints, and the consistency loss:
L.. >2(a ||U & [s, I12 + 3 [eu - [e, :11
(7)
where a = 1 if the s-th scene has been learned in the previous scenario {= and 8 = 1 if the e-th environment also has been learned before; and X2 is the consistency balance parameer..
Exploration Scenario-Speciffe Knowledge. As illustrated in Figure 4 when learning subsequent scenario task T, = (S.. E.).t t continually, we freeze the experts { to keep previous expert knowledge intact, and only train the specific expert [s, " :] in-
(TU [i, (U+[j. [1]+te)
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(a) Normal Envronment
(b) Lowiight Environment
(6) Overexposed Eniiroormmnt
(d) Scatering Environment
Figure 5: Illustration of navigation scenario examples of the Allday- Habitat simulation platform. includes four common environments: (a) normal, (b) low-light, (c) overexposure, and (d) scattering.
crementally to learn the task-specific decoupled knowledge. To learn task-knowledge more effectively and independently, we perform the orthogonal optimization on the scene expert [s, |] or environment expert U+[e, :]. Specifically, during adapting the t-th scenario task, we prefer that the scene expert U [ or the environment expert U [e,,, | be orthogonal to the previous scene experts Σ [, U =)) 0 or environment experts Ej= (U* U :)) = 0, learn specific knowledge more thoroughly. Thus, the task-specific expert subspace orthogonal constraint is: (1-
L.,==A((11 -10) -1-
Norm(U -
"'o=NN)rmUU),, (8) Ú
where Norm ( :)))))/| =II?? (i= (i==...., m) denotes the normalization of each row for U to have unit Euclidean norm, A3 is the balance hlper-paramen for orthogonal constraint L...
In summary, during the lifelong navigation learning process for task Tr, the adaptation loss for the LLM--ased navigation agent F performing auto-regressive action generation training is as follows:
N
C. = - Σ
Pe PuII,O.) + Lok + Lco + Les:
n=1
where Pr(An, Pn)z,o) o) denotes the predicted probability with n-th annotation action under agent's current observation IO, {Ie, O.}. and the balance syper-parameter is X 1 (A1 + d2 + Aa).
3.4 TASK-SPECIHIC EXPERTS SEARCH
To accurately invoke scene expert and environment expert during inference, we store retrieval fea- tures based on the CLIP vision encoder V(O) for each scene S and each environment & during the continual training phase. Specifically, during training, store the vision features Fc, VOO)) for each scene to form a scene feature set {Fe.t, Fe.,..., Fe.M). and the vision features Fee (O..) for each environment form environment feature {Feat, Fee2,..., FeeN J. During in- ference in an unknown navigation scenario Sq. we perform a two-step matching to determine the selection of the specific scene expert and the specific environment expert. Specifically, we extract the vision features Fe,, = V(O,)) of the agent's 's observation O₂ in the unknown scenario Sy- Then we match the scene expert [s, where s = arg max Simm Feg: .Fe...... FEAM }), and we also match the environment expert Ue[,,:]. where e = arg max Sim Feg. {Fe.1 .... Fe }), and the Sim(.,.) denotes the cosine similarity between the input element and each element of the input set.
ALLDAY-HABITAT SIMULATION PLATFORM
To train and evaluate the proposed ALL-VIN task, we extend the embodied AI simulation platform Habitat (Savva et (2019)) by expanding its simulation scenarios from a single normal environment to diverse degraded environments, including normal, scattering, low-light, and overexposure condi- tions. We synthesize the degraded environment from the normal environment based on three imag- ing models. Specifically, i) to synthesize scattering environments, we perform degradation synthesis based on the atmospheric scattering model (Narasimhan & Nayar (2000); Wang et al. (2024c)). The model is used to describe the imaging process in scattering environment, which can be expressed:
I(x,) J(z))e- 9d(x.) + A(1 -d((2)))
(10)
where [xx;) denotes the pixel value of pixel point x; in degraded scattering image, and J(x,) denotes the clear image in normal environment. t(x;) e -Bd((n)) denotes the medium transmission map, where d(x,) is the scene depth and 3 is the scattering density coefficient. is the global atmospheric light. ii) Moreover, synthesize the low-light environments, referring the abnormal

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Published as a conference paper at ICLR 2026
light imaging models (e.g.. Healey & Kondepudy (2002); Wang et al. (2024d); Cao al. (2023)
Wang et al. (2023a)), our formation model for a light degradation can be expressed as follows:
[xx;)= - ((E))CCF(5(z:)) + (xa)), -(z))--- T L(x)), N(x;) = Naha (x;) + Nread (Za),
(11)
where I (x;;) denotes the pixel value at location 2;, and CRF( t is the camera response func- tion, which introduces a nonlinear Gamma mapping from the sensor irradiance to the digital output. And the signal term S(x) consists of the system gain G that converts photoelectrons into digital units, scene irradiance L(x), and exposure time T', And the noise term N (x) consists of the photon shot noise Nhhhe (x) (Poisson distributed, with variance proportional the signal intensity), the read- out noise (x) (Gaussian distributed, signal-independees environments, our formation model for an overexposure degradation can be expressed as follows:
iii) To synthesize the overexposure
I (xx) CRF (clip(G T L(x,) + Satoo (x;,) + Nn,d( O, Ssa))
(12)
where L(x;) is the scene irradiance, Sat denotes the sensor saturation level, and clip( 0, Ssa) stricts the signal within the valid dynamic range. Based on the aforementioned three imaging mod- els, synthesize three degraded scenarios, with examples shown in the Figure 5 And the specific parameters and implementation details of the degradation models can be found our Appendix $
EXPERIMENTS
5.1 IMPLEMENTATION DETAILS
The Proposed All-day Multi- scenes Lifelong VLN Bench-
Continual Leamiing in the Environment Dimension
Loosight Oreweoouus
Tark17
Taské
mark Settings: As described
the Allday-Habitat ($
4 and
Tack18
Tiak11
Task15
T2584
Tosk11
Task23
Task22
our Problem Definition (S (2.
construct munti-bierarchical
navigation task settings to eval- uate our proposed Allday Walker. Specifically, the proposed bench- mark shown in Figure 6 con- sists of 24 hierarchical naviga- tion task scenarios. These sce- narios include five distinct sim- ulation scenes, each containing four environments: normal, low light, overexposure, and seat- tering. And the scenarios also include two real-world scenes, each containing four environments: normal, low light. All the scenarios before proceeding to navigation inference. Please note that for more tailored practical real-world navigation applications, the task-id f. is seen during training but is agnostic during the testing phase.
Tank
Taxk22
Tank16
Task20
Task12
Twak1
Twnk?
Task3
Task14
Tasks
Tank21
Tawk24
Narmal
SeHeCaTTO
Confinusl Learning in the Scene
Dimension
Figure 6: Illustration of our all-day multi-scenes lifelong VLN Benchmark. Agents are required to perform continual learning across two dimensions: scene and environment. The order of tasks is randomized. Further details please refer Appendix $
trained sequentially
The Used Training and Evaluation Settings:
For fair comparisons, both our Allday Walker and all comparison methods implemented on the Stream VLN agent (Wei et al. (2025), ). Training and evaluation are conducted on eight NVIDIA RTX 6000 Ada GPUs using PyTorch 2.1.2. We use the Adam optimizer with initial learning rate of 1.0 10-4 The low-rank settings of our TuKA
"1 r2 8, 64, = 64, with the number of experts set to M = 7, = 4. X = 0.2, X = 0.2, As 0.1 and settings. All hyperparameters are summarized in our Appendix C Following (Zheng et al. (202)); Wei al. 2025): Anderson et al. (2018)), we report three standard evaluation metrics: success rate (SR), success rate weighted by path length (SPL), and oracle success rate (OSR). To further assess resistance to forgetting in lifelong learning, we introduce three additional measures: SR forgetting rate (F-SR), SPL forgetting rate (F-SPL), and OSR forgetting rate (F- (F-OSR), and they are defined:
0.95. All other hyperparameters of VLN agent follow the Stream
F-SR,= M-SR, M1-SR-SSO SR, M-SR,
---SPL M-SPI, SPT, F-OSRR M-OSR, -OSR, M-OSR,
(13)
M-SPL
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Table 1: Test Results (SR in %) of Comparison Experiments under the AML.-VNN Settings.
Comparisons
[T
T2
T3
14
T5
T6
T7
TУ
T10
T11
T12
T13
T14
T15
T16
T17
TI8
T19
T20
T21
T22
T23
T24
Avg.
Sey-FT
0
0
5
10
7
6
7
14
4
24
28
24
64
11
LwF-LoRA
5
0
4
14
8
4
8
14
9
5
25
31
12
EWC-LoRA
10
6
4
10
15
14
14
10
6
12
19
16
14
8
24
28
29
38
67
15
MoL.F
18
29
20
Spurie MoLE
29
10
24
29
17
28
29
11
37
24
23
30
18
24
25
34
31
10
14
38
36
36
45
69
28
32
21
24
32
33
HydraLoRA
45
12
39
43
24
33
34
29
57
38
33
34
29
37
32
42
38
28
19
52
46
42
48
70
38
BranchL.RRA
43
39
57
65
43
33
53
37
33
24
62
52
69
44
O-LoRA
19
47
58
31
42
67
67
49
38
68
52
52
34
71
59
71
52
SD-LoRA
22
52
48
71
71
62
42
75
56
49
36
69
64
69
56
FSTTA
18
46
35
58
51
51
43
34
56
46
41
29
52
45
41
47
44
FeadTTA
58
19
53
62
27
41
65
30
59
56
59
50
39
64
54
58
51
48
34
61
52
45
48
55
50
Allday Walker
23
71
81
50
87
38
79
75
79
67
50
86
71
76
63
43
81
68
58
72
65
Table 2: Test Results (F-SR J in %) of Comparison Experiments under the AML- Settings.
Comparisons
T6
T8
T9
T10
T13
T15
T18
T23
Avg.
Seq-FT
100
100
100
100
100
100
93
100
88
95
94
83
91
92
92
83
87
89
94
73
84
81
78
0
87
LwF-LaRA
92
100
91
100
100
100
92
89
81
93
95
78
88
89
83
84
71
82
73
S4
EWC-LoRA
85
91
82
75
66
Dense MoL.t
80
91
82
75
78
78
81
86
77
81
78
72
73
79
72
63
69
85
63
74
64
61
0
72
Sparse McLE
68
69
64
72
49
58
82
56
68
64
56
68
70
58
82
54
63
56
62
62
68
Hydral DRA
48
51
46
60
40
26
67
31
49
49
39
61
63
50
44
49
29
46
38
46
BranchLoRA
34
46
36
29
13
52
29
48
29
23
58
18
54
32
42
12
36
29
36
O-LeRA
24
17
20
SD-LoRA
12
34
34
13
36
12
37
-2
36
12
26
13
28
17
18
AlldayWalker
23
0
28
9
30
24
10
18
12
where M-SR,, M-SPL, and M.OSR, denote the performance (SR, SPL, OSR) obtained when training solely on navigation tasks through I, i.e., training on {Ti, T2, Te), S 20. Thus larger values of F-SR, F-SPL,, and F-OSR, R, indicate higher degree of forgetting in the t-th task.
5.2 COMPARISON EXPERIMENT RESULTS
This experiment evaluates the navigation capability of our proposed Allday Walker. We compare it with range of state-of-the-ae LoRA-based continual learning methods: Seq-FT denotes sequential fine-tuning over all tasks; LwF-LoRA (Li & Hoiem (2017)) applies knowledge distillation to retain prior knowledge; EWC-LoRA (Xiang et al. (2023) regularizes parameters critical to past tasks to mitigate forgetting; Dense MoLE (Chen et al. (2024)) adopts dense expert routing, while Sparse MoLE (Dou et al. (2024)) introduces sparse expert routing within MoE-LoRA; MoLA (Gao et al.
2024 enhances Sparse MoL.E by incorporating deeper expert hierarchies; O-LoRA (Wang et al.
2023c) leverages orthogonal loss to disentangle task-specific features; Hydral LoRA (Tian et al.
20244) shares a global module A for common knowledge while employing multiple B modules for :isssppecialization; BranchLoRA (Zhang et al. (2025a)) further strengthens the sparse routing mechanism; and SD-LoRA (Wu et al. (2025) adaptively composes LoRA modules from previously learned skills. To keep the number of trainable parameters comparable across comparison methods, the task-specific LoRA uses a rank of r = 6, and MoE-LoRA applies r = 16 with K 8 experts, 32 with K 8 experts. The implementation details and methods parameter comparison are provided in Appendix C We also compare with FSTTA (Gao al. ) and FeedTTA (Kim et al.). which aim perform small, temporary adaptation during test time to adapt the agent to distribution shifts in a single new scene. The results on SR and F-SR are presented Table 1 2 And results on SPL., F-SPI., OSR, and F-OSR are presented in Figure Based on the results, our AlldayWalker Walker achieves consistent superiority across various metrics.
whereas MoE-LoRA shared A applies
5.3 ABLATION ANALYSIS
We provide ablation analysis to validate the effectiveness of TuKA. Unless otherwise specified, all ablations are trained on the 20 simulation tasks and evaluated on the same ID-unseen 20 tasks.
Does the third-order tensor well represent the multi-hierarchical knowledge? Our Allday- Walker uses a fourth-order tensor based TuKA to represent multi-hierarchhcal navigation knowl- edge (i.e., scenes and environments). In this section, we explore whether third-order tensor
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Sevetec (8)
MAY Mottrie
Comparison with SPL
(b) Comparison with F-SPIL
(c) Comparison with
(0) Comparison With F-OSR
Figure 7: Test results ((a) SPL 1. (b) F-SPL 4 (c) OSR t. (d) F-OSR L) of comparison experiment under the AML-VLN settings. For detailed quantitative results, please refer Appendix $
3-order SR
4-order SR
36
70
75
71
72
63
70
60
40
30
20
Figure 8: The comparisons of tensors for representation multi-hierardee hierarchical knowledge.
sufficient to represent multi- caati-hierarchical knowledge. Specifically, construct third-order tensors X'ee ga x (N) and perform Tucker decomposition on it. We employ U (MMM))))) the navigation scenario expert, following the DKIL strategy to learn lask-specific knowledge with each row. For ing across the 20 simulation tasks using the same training method as for fourth-order tensors total of 20 rows of experts, U 2001128) The ablation results summarized in Figure H Based on the results, fourth-order tensors achieve superior performance across all 20 tasks. This suggests that, compared third-order tensors which represent multi-hierarchiie. knowledge through coupled expert set structure, fourth-order tensors employ a decoupled representation of multi-hierarchical knowledge. This natural structure facilitates both shared and task-specific knowledge learning. We also explore more hierarchical knowledge representations with fifth-order tensors in Appendix $
detailed description, please refer
Appendix H We perform continual learn-
Why share the core tensor, encoder and decoder? We explore the effects of all tasks with shared core tensor 9, encoder U² and decoder U the ablation results are summarized in Table 3 Specifically, the "Sd-G" denotes the stored TuKA has only a core tensor G: If without "Sd-g", denotes store specific "Sd-g," for each task Tr. Similarly, "Sd-U/"" denotes the stored TuKA has only a shared encoder U2 "Sd-U1" denotes the stored TuKA has only shared decoder U Based on the results (w/o Sd-U²2, w/o Sd-g), both the shared core tensor and encoder between tasks contribute representing shared knowledge across tasks, thereby improving life- long navigation performance. Although shared the decoder do not provide noticeable performance improvement (w/o Sd-U11, shared to contribute to the integrity of tensor representation, and significantly reduces storage consumption (no multi-decoders. stored), thus our TuKA shared U
Table 3: Ablation Results (%) for Shared Components.
Sd.g
Sa
Sd.U/2
F.SR
SPL
X
X
53
10
47
17
56
8
65
11
58
18
69
x 63
11
55
67
Does continual learning for more tasks lead to catastrophic forgetting? Our tasks already cover diverse scenes and four distinct imaging conditions across both simulation and real-world environments, forming a sufficiently challenging lifelong learning benchmark. To further validate the continual learning performance of Allday Walker when dealing with tasks. We also conduct additional continual learning experiments by adding two new real-world tasks and four simulation tasks. For additional task scenes and environments, refer Appendix Table 22 The results

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Published as a conference paper at ICLR 2026
Table 4: Comparison of SR (%) under 24-task and 30-task learning.
Tasks
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
T15
SR (24 tasks)
79
23
71
81
33
50
87
38
79
75
79
67
50
86
71
SR (30 tasks)
77
23
71
70
33
50
86
38
79
75
80
66
50
85
70
Tasks
T16
T17
T18
T19
T20
T21
T22
T23
T24
T25
T26
T27
T28
T29
T30
SR (24 lasks)
76
67
63
43
68
58
62
72
SR (30 tasks)
77
68
44
68
58
66
72
76
67
61
Table 5: Generalization performance SR (%) across unseen environments (GI-G6).
Task
Scene
Environment
Test Number Stream SR BranchLoRA SR
SD-LoRA SR Allday SR
G1
YcPG
Normal
105
52
53
65
G2
ur6pFq6Qu1A
Low-light
120
45
G3
147D
Scattering
105
40
41
54
G4
Vvot9LyI1Cj
Overexposed
108
42
39
G5
Real-Wordh 4
Normal
100
38
37
G6
Real- World 5
Low-light
100
18
21
21
43
Avg.SR
35
40
39
55
summarized in Table 4 The results show that incorporating more tasks does not lead to noticeable performance degradation, demonstrating that Allday AlldayWalker remains stable under lifelong learning with more tasks. The visualization of these navigation tasks is shown in Appendix Figures [3]
Can our Allday achieve generalization to unseen scenarios? We also explore the pro- posed Allday Walker's generalization performance on unseen scenarios compared to other methods. Specifically, we perform six completely unseen tasks for generalization testing. Select the expert with the highest similarity during testing. These six tasks include four simulation scenarios (four distinct scenes with four distinct environments) and two real-world scenarios (two distinct real- world scenes in low-light and normal environments). The details of the unseen task scenarios and results are summarized in Table [ The results show that our AlldayWalker has superior general- ization performance, achieving average SR of 55%, surpassing SD-LoRA (39%) by 16% and BranchLoRA (40%) by 15%. The visualization these tasks is shown Appendix Figures 16 16
We provide more ablation analyses for TuKA scaling, extension, and effect in Appendix G
CONCLUSION
We formalize the all-day multi-scenes lifelong vision-and-lane ann-lang navigation (AML-VLY) leam- ing problem to study VLN agent lifelong adaptation across multiple scenes and diverse environ- ments. To address AML-VLN, we propose Tucker Adaptation (TuKA), a new parameter-efficien method that represents the multi-hierarchiie knowledge in a high-order tensor and uses Tucker de- composition decouple task-shared and task- sask-specific knowledge. We further propose decoupled knowledge incremental learning strategy to support multi-hierarchical knowledge continual learn- ing. Based on the proposed TuKA, we also develop a lifelong VLN agent named Allday Walker, which achieves superior navigation performance compared to the SOTA baselines on the AML- VLN problem, enabling all-day multi-scenes navigation. Our research demonstrates the value of high-order tensor adaptation for continual multi-hierarchical knowledge representation learning.
ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation of China under Grant T2596040, T2596045 and Ú23A20343, CAS Project for Young Scientists in Basic Research, Grant YSBR-041, Liaoning Provincial "Selecting the Best Candidates by Opening Competition Mechanism" " Science and Technology Program under Grant 2023JH1/10400045, Joint Innovation Fund of DICP & SIA under Grant UN202401, Fundamental Research Project of SIA under Grant 2024IC3K01, Natural Science Foundation of Liaoning Province under Grant 8025-BS-0199
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APPENDIX
A RELATED WORKS
Iision-and-Lanuuse Navigation (VLN). VLN is an important embodied AI task where agents fol-
low natural language instructions to navigate environments (Wang et al. (2025) ; Qiao et al. (2025); ;
Zhao et
(2025): Gao et al. (2025b); Dong et al. (2026): Wei al. (2025): Zhang et al. (2025e;;
Han et al. )) Since the introduction of the simulator and Room-to- Room benchmark dataset
(Anderson et al.
(2018). ), research has evolved rapidly. Early works relied on discrete navigation
graphs (Ku et al.
2020); Qi et al. 2020)) ) which was able to reduce the action solution space of the
agent but limited transfer to real-world continuous spaces. To address this, the VLN-CE benchmark
(Krantz et al. (2020) extended VLN into continuous environments, enabling fine-grained actions
WWange et al. (2023b) and waypoint-basee planning (Hong et al. (2022); Krantz et al. (202T); Krantz
& Lee (2022h). With the emergence of large-scale embodied datasets (Chang et al. (201 70; Ramakr-
ishman et al. (202I) and advanced simulators (Savva et al. (2019): (olve et al. (201 Makooviych
al. (2021 1 VLN research has shifted toward more realistic, long-horizon navigation settings, and
more complex navigation environments. Recent works integrate large pre-trained vision-language models to enhance reasoning capability and generalization capability (Zhang et al. (2024b); (Zheng]
itt al. (2024); Zhang et al. (2025b); Gao et al.
(2025a); |Wei et al. (2025)) while others exploit his- tory modcling (Qiao et al. (2023; Chen et al. p0 Anwar et al. (2024) 4) or spatial memory maps (Krantz et al. (2023); Wang et al. (202ee); Hong et al. (2023); ; An et al. (203)). And the recent
Tube-VLL. (Lin et al.) (2023) improve VLN policy learning through foresight modeling, meta-ability guidance, or video- driven pretraining. Despite significant progress, existing VLN agents still face challenges in practical deployment: when adapting new environments (e.g., day/night, scattering conditions), they often suffer from catastrophic forgetting of previously Iearned scenarios, severely limiting their robustness in dynamic real-world settings. Test-Time Adaptation (TTA) VLN methods (Qiao et al. 2023 ; Kim
methods NavQ (Xu al. (2025)) MAGIC (Wang et al. (2024ap) and
et al. Gao et a (2023)) aim to perform small, temporary adaptation during test time to adapt the agent distribution shifts in single new scene. FeedTTA (Kim et al.)) improves plasticity to new scenarios while selectively correcting certain gradients, thereby suppressing drastic parameter drift caused by non-stationarity to reduce forgetting of previous tasks. The strength of these methods is that agents can quickly adapt to new scenarios. However, they do not accumulate knowledge over continual tasks. They do not maintain any explicit scene or environment knowledge after a single episode ends. They are primarily designed for rapid adaptation in short-term scenarios rather than long-term, multi-scenario persistent operations.
Lifelong and Continual Learning. Lifelong learning (Li et al. 2025) Yang et al. 202.5b Wang jet al.) (2024b) 2026b): [Dong et al. (2025); [Liu et al. (022): : Liang et al. (2024) seeks enable models to continually acquire new capabilities without erasing past knowledge. Existing approaches can be grouped into three categories: (i) regularization- based methods, which constrain weight up- dates to protect important past-task parameters (Derakhshani et al. (2021); Douillard et al. (2020);
Li & Hoiem| (2017); (ii) architecture- based methods, which expand the network or allocate new modules for different tasks (Jung et al. (2020); Toldo & Ozay (002); Wang al. (202): ; Wu al. (202 ); and (iii) replay-based methods, which rehearse stored or generated past samples BBng et al.
2021 Li al. (2022); RRbuffi et al. (2017): : et al. (2024; 2022); Wan al. (0224): ; Xiang et al.
2019 While effective in standard benchmarks, these methods face difficulties in embodied tasks where memory replay is costly and task boundaries are often unclear. Recently, parameter-efficient fine-tuning methods such as LoRA (Hu et al. (2022)) have been adapted for continual learning due to their modularity and separability. Several variants, including HydraLoRA (Tian et al.) (2044). Branchl (Zhang et al. (2025a)) MoLA (Gao et al. (2024)), and Sparse/OLoRA (Dou et al. (2024): Wang et al. (2023c). ) explore expert-based sharing mechanisms to balance efficiency and adaptability. However, these methods fundamentally rely two-dimensional low-rank matrices, which struggle to capture the multi-level navigation knowledge (shared action semantics, scene- specific, and environment-speeifee factors) required in AML-VLN.
Tensor Decomposition for Representation Learning. Beyond matrix-based LoRA, tensor meth- ods provide a new framework for modeling matti-hierarchicall structures. Classical tensor decompo- sition methods, such as CP and Tucker decomposition (Kolda & Bader (2009), ), have been widely
studied in signal processing and multi-way data analysis (Liu et al. (2026)). Recent explorations
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in neural networks investigate using tensors to compress parameters Jahromi & Orús (2024); Ber- shatsky et al. (2024) or to model structured attention mechanisms (Zaagg et al. (2025d)). FiPS (Uyük et al. (2024) combines sparse tensor decomposition and parameter sharing compress and share parameters across layers in ViT' and L.Ms,, demonstrating that parameter sharing via high- maintain performance with heavy compression. MetaTT (Lopez-Piqueres itt al. (2025) uses Tensor-Train decompositions globally across multiple linear sub-modules of transtormers, compressing adapters with Tensor-Train structure rather than separate per-layer ma- trix adapters. However, these methods typically treat specific architectures (e.g., multi-head atten- tion) as tensors rather than aligning high-order tensors with the full LLM backbone. Consequently, they do not fully realize representation learning in higher-order spaces. In contrast, our work lever- ages Tucker decomposition to explicitly factorize navigation knowledge into shared components and task-specific experts (scene and environment), offering principled way with multi-hierarchical knowledge for robotic lifelong VLN.
order factorization
B NOTATIONS AND DEFINITIONS
To make it easier for readers, summarize some important notations and definitions used in this paper 6
C COMPARISONS IMPLEMENTATION
We provide detailed comparison experimental implementation. For a fair comparison, both our Allday Walker and all comparison methods are implemented on the Stream baseline (Wei et al.) (2025)) ). Training and evaluation
conducted on eight NVIDIA RTX 6000 Ada GPUs using Py- Torch 2.1.2 (cul21). We adopt the Adam optimizer with an initial learning rate of 1.0 x 10 The low-rank settings of our TuKA are
12 = 8, 64, F4 64, with the number of experts set to M = 7, N = To keep the number of trainable parameters comparable across comparison methods, the task-specific LoRA configuration uses rank of 6 with a total of 24 sets of LoRA, the single-LoRA configuration uses a rank of r = 128, and MoE-LoRA applies r = 6 with K = 24 experts, whereas MoE-LoRA shared A applies r 12 with K 24 experts. Specifically, for proposed TuKA, its parameters are calculated as follows:
ParamтaKA Paramg + Paramur + Paramu= + Paramus + Paramo
= (ri x 12 x r3 X T4) + (a x m1) + (b r2) (M x r3) + (N x 14)
(8 x 8 64 64) + (1024 8) + (1024 x 8) + (7 64) + (4 x 64)
279, 232 R 0.3.M.
(14)
And for the task-specific LoRA comparison methods (i.e., SD-LoRA, O-LoRA), their parameters calculated follows:
ParamLoRA = T x (Params + Paramy)
T' x ((a r) + (b x r))
X ((1024 x 6) + (1024 x 6))
294, 912 ~ 0.3M.
(15)
And for the single-LoRA comparison methods (i.e., LwF-LORA, EWC-LoRA), their parameters
calculated as follows:
ParamLoRA Parama + Paramp
(a x r) + (b x r)
(1024 128) (1024 x 128)
=262, 144 x 0.3M.
(16)
And for the MoE-LoRA comparison methods (i.e., Dense MoLE, Sparse MoLE, MoLA), their pa- rameters are calculated as follows:
ParamMoE- LORA = K x (Params + Paramg)
K ((a r) + (6xr))
=4 X ((1024 x 6) + (1024 x 6))
294, 912 0.3..
(17)
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Table 6: Some Important Notations and Definitions Used in This Paper.
Notations
Definitions
Notations
Definitions
I
The user language instruction
The Hadamard product
8
Navigation Scene
For
The Fisher information weights for U
E
Scene Environment
F
The Fisher information weights for U/2
JF
The backbone agent
V(O.)
The vision features for each scene
0
The video stream observation
V(O.)
The vision features for each environment
V(O)
The CLIP vision encoder
y
The oulput navigation actions annotations
i
The navigation step
w
The exponential moving coefficient
F(V(O))
The next action
Fo.,
The smooth update of Fisher
T
A sequence of navigation taks
W
The i-th layer with backbone weights
A'
low-rank dimension-redlucnng matrix
5
An unknown navigation scenario
B²
low-rank dimension-raising matrix
Og
The agent's 's observation
X
A high-order tensor
J(zs)
The clear image in normal environment
T = (S.,
The 1-th navigation secnario task
d(x))
The scene depth
Fe,
The base navigation agent
The scattering density cocfficient
AW!
The updated weight I-th layer for 2
A
The global atmospheric light
x
A four-order tensor
CRF(F)
= 5"
The camera response function
g
core lensor
S(x)
The signal tern
U
The factor matrix represents the transformation pattern of the feature from F1 dimension a
M(x)
The scene irradiance
U2
The factor matrix represents the transformation puttern of the feature from b dimension to ity
T
The exposure time
3
The factor matrix represents the M group of scene experts
[x)
The noise term The photon shot noise
U
The factor matrix represents group of environment experts
Na)
And for the MoE-LoRA (i.e., BranchLoRA, HydraLoRA) shared A comparison methods, their pa- rameters are calculated as follows:
ParamMoE-Late LORA
Parama + K x ParamB
r) + K (b x r)
=(1024 x 12) + 24 x (1024 x 12)
=307, 200 z 0.3M.
(18)
Therefore, parameters across comparison methods are maintained at a consistent order of mag- nitude and remain comparable, ensuring uniformity in learnable parameters and guaranteeing ex- perimental fairness. All other hyperparameters of the VLN agent follow the original Stream VLN settings, and we summarize some important parameter settings as shown in Table 7 During lifelong learning, to obtain the Fisher Matrix, we compute using the first 10% of the data before adaptation
to each task, using Eq. 5 In line with prior work (Zheng et al. 2024); Wei et al. (2025): Ander- son et al. (2088). we mainly report three standard evaluation metries: SR, SPL, OSR. To further assess resistance catastrophic forgetting in lifelong learning, we also introduce three additional
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measures: SR Forgetting Rate (F-SR), SPL Forgetting Rate (F-SPL), and OSR Forgetting Rate (F-OSR). The specifie calculation is outlined below.
Navigation Success Rate (SR). The navigation success rate is a widely adopted metric for eval- uating whether an embodied agent successfully reaches its target location. It is defined based on the Euclidean distance between the predicted final position of the agent and the ground-truth goal position. A navigation attempt is considered successful if this distance is smaller than or equal to a predefined threshold Formally,
11, if d(TT"ed, Se 10, otherwise,
SR =
(19)
where Tpred is the predicted endpoint, Tref is the reference endpoint, and d(-.-) denotes the geodesic distance. SR provides a binary indicator of task completion quality and reflects the agent's reliability in precisely reaching the goal.
Oracle Success Rate (OS). While SR only evaluates the final stopping point, the Oracle Success Rate captures whether the agent's 's trajectory passes sufficiently close to the goal, regardless of the final decision to stop. This metric therefore measures the feasibility of correcting the navigation path with ideal stopping policy. It is defined as:
OS = f1, 1, if minse[L.n) d(T,,T) S, 0, otherwise,
(20)
where Te is the ground-truth target location, and T, denotes the i-th point along the trajectory. Com- pared to SR, OS relaxes the evaluation by rewarding trajectories that approach the goal, even if the final stopping action is inaccurate.
Success Rate weighted by Path Length (SPL). The SPL metric provides more comprehensive evaluation by jointly considering task success and path efficiency. Specifically, it penalizes trajec- tories that are unnecessarily long compared the reference path, thereby encouraging agents to follow efficient routes. Formally,
SPT. Success Rate x TL
(21)
Thref
where TL is the actual trajectory length executed by the agent and T Lyer is the ground-truie shortest trajectory length. A high SPL score requires not only reaching the goal but also doing so along a near-optimal path, making it stricter and more informative metric than SR alone.
During testing, we perform 100 episode and calculate the average value of the matrices. To evalu- ate the navigation capability of our proposed state-of-the- the-rr LoRA-based continual learning baselines. Below we summarize their main imple- mentation details:
we compare it with broad range of
Seq-FT:
Sequential fine-tuning of the base model with a vanilla LoRA on each task without any forgetting prevention mechanism. After training is complete, use the stored vanilla LoRA for inference.
LwF-LoRA (Li & Hoiem (017) )) Extends Learning without Forgetting to vanilla LoRA
by applying knowledge distillation retain prior task knowledge during training new tasks, and we set the parameters in accordance with the literature (Li & Hoiem (017))
After training complete, use the stored vanilla LoRA for inference.
EWC-LoRA (Xiang et al. (202)): )) Incorporates Elastic Weight Consolidation into vanilla
LoRA, constraining updates of parameters that are estimated to be important for previous tasks, and we set the parameters in accordance with the literature (Xiang al. 2023 )
After training is complete, use the stored vanilla LoRA for inference.
Dense MoLE (Chen et al. (2024)): Uses a dense mixture-of coitture-of-Loofe -eeA-eteetttts where all ex- perts are activated for every task, capturing more cross-tate task knowledge at the cost of higher computation. After training is complete, use the stored MOF-LoRA for inference.
Sparse MoLE (Dou et al. (2024)): Improves efficiency by sparsely routing inputs only a subset of LoRA experts for each task, by selecting only the top-k experts per instance (k: = 2). After training is complete, the stored MOE-LoRA for inference.
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Algorithm Training pipeline of Allday Walker (TuKA + DKIL)
1: Prepare task stream T (Ti....., Tr where T, {St, Ee).
2: Learnable factors: core g, encoder/decoder U , U² scene experts U environment experts U
3: for = 1 to 7 do
4: Get current scenario (s, e) from T
5: it't then
Initialize g, U U² by Kaiming initialization. 7: Initialize [ [e,, :] with pero-initialization.
6:
8: else
9:
Inherit the current shared parameters g, U U from previous tasks learned g', U U² If s-th scene or e-th environment appeared before, inherit the current (8, :I, U [e, from previous U [s, :), U [e end if
10:
11:
Freeze all non-current experts (U :llist (U Compute Fisher Matrix F. with 5
13:
Update and save Fisher Matrix Fe with Eq/ for each minibatch (Oi, I;) do
15:
Reconstruct adapter Á W by Tucker decomposition with (g, U1, U2, U2 :]. U4 :]).
17:
Compute Tewwe (shared knowledge consolidation) using Fisher matrices with Eg Compute L.co (expert consistency) with Eq and and T.es (orthogonality) with Eq Compute auto-regressive loss CAR from predicted actions with Eq Total loss: CAR + Lik + Loo + Les-
19:
20:
21:
Update trainable parameters {9, U1. U2, Us[s, Uale,:)) with Adam.
22:
end for
23:
Save CLIP features for (s) retrieval keys for inference ($ 3.4
24:
end for
MoLA (Gao et al. (2024)): Builds on Sparse MoLE by adding deeper hierarchical expert layers, allowing richer task-specific adaptations. We increase the number of deep experts to 16, whilst reducing the number of shallow experts to After training complete, use the stored MOE-LoRA for inference.
O-LoRA (Wang et al. (2023c)): Stores one task-specific vanilla LoRA module per task and employs an orthogonal loss encourage disentangled task representations. This method requires maintaining T LoRA modules for 7 tasks. After training is complete, it employs selection inference based on the proposed expert selection methodology ($ 3.4).
HydraLoRA Tian et al. (2024)): Shares global module A across all tasks to encode common knowledge, while employing multiple task-specific modules for specialization. After training complete, use the stored HydraLoRA for inference.
BranchLoRA (Zhang et al. (025a)): ): Enhances sparse expert routing by introducing branching mechanism. enabling more flexible expert selection across diverse tasks. After training is complete, it employs task-specific B selection inference based on the proposed
expert selection methodology ($ 3.4).
SD-LoRA (Wu et al. (025)): Stores T task- LoRA modules and dynamically com- poses them during inference, transferring knowledge from previously learned tasks, ac- cordance with the literature (Wu et al. (2025)). After training is complete, it employs selec- tion inference based on the proposed expert selection methodology ($ 3.4).
D ALGORITHM SUMMARY
For case of readers understanding, a summary of the proposed Allday Walker learning algorithm is provided in the Algorithm [ and summary of inference algorithm is provided in the Algorithm
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Algorithm 2 Inference pipeline of AlldayWalker (TuKA)
1: Input: instruction Ig. current observation Og.
2: Encode O, with CLIP to get feature J4 - En(O,),
3: Match scene expert index
arg max; Sim(Jqs (Fest, Fes2. FesM))
4: Match environment expert index = arg máx, Sim ( {Feet Feez, FeeN]):
5: Reconstruct adapter AW using G, U1. U2 Us (sss Usle.:]
6: Adapt base model: 0 = 0n + AW.
7: Run auto-regressive decoding with (I, O) to generate navigation actions.
8: Execute actions step by step until the goal is reached.
Table 7: Some Important Hyper-parameters in Allday Walker.
Hyperparameters Name
Value
Number of scene-speeific experts S
7
Number environment--pecifee experts 4D Tucker decomposition ranks (21, Pa. r4)
(8,8,64,64)
Elastic Weight Consolidation regularization weight A1 Stabiiity constraint weight for previously trained experts A
0.2
0.2
Orthogonal regularization weight for U3/U4 X
0.1
Number of samples for Fisher matrix computation Fisher matrix exponential moving coefficient Number of training epochs per task N Nepoche Training batch size per device Birain
20
0.9
10
Base learning rate !]
le-4
Number of historical frames Nuist Number of future steps to predict Nfat Number of frames per video Nrrmes
8
4
32
E ALLDAY-HABITAT SIMULATION PLATFORM
To validate the proposed AMI.-VIN task, we construct a multi-hierarchical navigation task settings to evaluate proposed Walker Specifically, the proposed benchmark shown in Figure consists of 20 simulation navigation task scenarios. These simulation scenarios include five distinct scenes, each containing four environments: normal, low light, overexposure, and scattering. All the scenarios are trained sequentially before proceeding to navigation inference. We provide the specific parameters and implementation details of the degradation models 4.. We summarize the used parameters in Table 8, And the statistics for the constructed dataset are shown in Table 9
F ROBOTIC NAVIGATION PLATFORM
The robotic navigation platform used in this work is illustrated in Figure 9 The platform consists of DeepRobotDog Lite2 quadruped robot, Hikvision DS-B12 camera, a portable WiFi commu- nication module, and a remote computation server equipped with an NVIDIA A6000 GPU. During deployment, the Hikvision DS-E12 mounted on the robot captures RGB visual streams from the real environment, providing essential perception signals for navigation and scene understanding. These visual data are transmitted real time to the remote server through the portable WiFi module. The server performs inference using the proposed Allday system running on the A6000 GPU, which processes both user instructions and visual observations to generate navigation actions. The resulting control signals are then sent back to the DeepRobotDog Lite2 via the wireless communi- cation channel. The robot executes these actions in the physical environment, enabling closed-loop interaction between perception, language reasoning, and embodied control. This platform supports flexible and robust experimentation of all-day multi-scenes lifelong navigation, bridging simulation and real-world deployment.
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Table 8: Some Important Degradation Imaging Model Parameters.
Pattern
Hyperparameters Name
Value
Low-night
Brightness factor
0.15
Exposure time T Sensor gain ISO C
0.15
8.0
Shol noise factor Cksbot
0.4
Read noise standard deviation dread
3.0
Gamma correction value
2.2
Denoise strength D,
0.75
Detail preservation factor Pa
0.7
Scattering
Atmospheric scattering coefficient 3 Atmospheric light intensity (RGB) A
0.01
[0.95, 0.95, 1.0]
Maximum effective distance dmax
200.0
Particle size Tp
Overexposure
Exposure multiplier Te
2.5
Sensor gain
1.5
Sensor saturation value (full-well capacity) Saa
0.9
Read noise standard deviation read
0.015
Gamma correction value
2.0
Bloom strength B,
0.3
Color shift vector (RGB) Cstift
[1.0, 0.96, 0.92]
Table 9: Statistics of the 24 Sequential Tasks in Allday- Habitat Benchmark. Each Task Corresponds to One Scene under One Environment Type.
Task ID
Scene Index Number
Scene ID
Environment ID
Train Number
Test Number
ac267MwG77aT
Scene-2
Normal-1
455
120
2
5LpN3gDmAk7
Scene-1
Scattering-2
405
150
3
S9hNv5qa7GM
Scene-4
Normal-1
410
105
4
mJXqzFimKg44
Scene-3
Low-light-3
445
105
5
mJXgzFumKg4
Scene-4
Overexposure-4
410
105
6
ULsKaCPVEIR
Scene-5
Overexposure-4
390
120
7
SIpNN3DmmAk7
Scene-1
Normal-1
405
8
ac267MwG7aT
Scene-2
Overexposure-4
455
120
9
ULsKaCPVEJR
Scene-5
Normal-1
390
120
mc267MwG74T
Scene-2
Scattering-2
455
11
ULsKaCPVEJR
Scene-5
Low-light-3
390
120
12
ULsKaCPVEJR
Scene-5
Scattering-2
390
120
13
5LpN3gDmAk7
Scene-1
Low-light-3
405
150
14
mJXqzFimKg4
Scene-3
Normal-1
445
105
15
S9hNv5ga7GM
Scene-4
Low-light-3
410
105
59hNv5ga7GM
Scene-4
Scattering-2
410
105
17
mJXqzFimkg4
Scene-3
Overexposure-4
445
105
18
n<26MMw77TT
Scene-2
Low-light-3
445
120
5LpN3gDmAk7
Scene-1
Overexposure-4
405
150
20
mJXqzFinkg4
Scene-3
Scattering-2
445
105
21
real-world
S
Normal-1
400
100
22
real-world 2
Scene-7
Low-light-3
400
100
23
real-world 1
Scene-6
Low-light-3
400
100
24
real-world 2
Scene-7
Normal-1
400
100
G DOES A LARGER DIMENSION YIELD BETTER RESULTS (MODEL SCALING)?
To study the scalability of Tucker Adaptation (TuKA) and the capacity trade-offs between shared and task-specific factors, we performed rank-scaling ablation. The default TuKA setting used in the main experiments is: r1 === 64,
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Navigation System Architecture
Real-world System
Hikvision DS-E12
Hikvision DS-E12
Actions
cbout Uha stane 2013
WIFI
Perdeption
Computation Platform
User Instructions
DeepRobotDog lite2
RealdWorld Scane
DeapRobotDog lita2
Figure 9: Illustration of the proposed robotic navigation system.
SR
SPL
-SOSR
OSR
SR
SPL
-OOR
68
68
68
68
68
71
68
65
88
64
60
65
65
63
62
60
60
60
62
58
59
58
59
56
56
53
51
53
52
50
50
47
0.5
0.5
(a) Uniform scaling
(b) Shared-only scaling (sos)
(c) Expert-only scaling (eos)
Figure 10: The analysis results for TuKA parameter growth for scaling of ranks.
with the number of scene experts M 5 and environment experts N 4. These default settings follow the implementation details in Appendix $C
We consider multiplicative scale factors [00,,2,3,44 and evaluate the following scaling schemes:
Uniform scaling (all): multiply (ri, r2, r3, r4) by 8, i.e. (s 8, 8 8, 64, 64).
Shared-only scaling (sos): multiply only (s T1,8 r2) by 8, keep (ra, r4) at baseline.
Expert-only scaling (cos): multiply only (s 15-33. T4) by s, keep (21.72) baseline.
For each configuration train AlldayWalker Walker under the same DKIL pipeline and evaluation protocol as in the main paper (same optimizer, learning rate, task ordering and evaluation metrics). We report averaged metries across the 20 AML-VIN tasks: Success Rate (SR), SR forgetting (F-SR) SPL, F-SPL, OSR and F-OSR. As shown in Figure TO] our proposed TuKA similar to vanilla LoRA in that increasing the rank in low-rank scaling experiments typically yields better results, yet this im- provement exhibits an upper bound. Detailed experimental results are presented in Table [0] Table Table 12 Table 13 Based on experimental results, we observe that specific components exhibit higher saturation values at upper limits compared to shared components. Consequently, we recom- mend prioritising expert dimensions over shared dimensions. Furthermore, owing to the higher-order properties of tensors, even linear scaling can induce substantial changes in adapter parameters. We therefore advise favouring adaptation with lower ranks.
Table 10: The Analysis Results for TuKA Parameter Growth for Scaling of Ranks (scale factors 0.5).
Rank P1
Rank ra,
Scale-Type
SR
F-SRI
SPL
F-SPL
OSR
F-OSRJ
4,4
32,32
all
60
11
51
18
62
9
4.4
64,64
sos
64
11
57
68
9
8,8
32,32
eos
61
11
52
18
63
9
8,8
64,64
cqual
65
11
58
18
68
9
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Published as a conference paper at ICLR 2026
Table 11: The Analysis Results for TuKA Parameter Growth for Scaling of Ranks (scale factors 8 = 2).
Rank 12
Rank 73,
Scale-Type
SR
F-SR!
SPL1
F-SPL
OSR
F-OSR.
16,16
128,128 128
all
68
61
71
9
16,16
64,64
sos
65
11
60
18
68
9
8,8
128,128
eos
67
61
71
9
8,8
64,64
equal
65
11
58
18
68
9
Table 12: The Analysis Results for TuKA Parameter Growth for Scaling of Ranks (scale factors = 3).
Rank 11,
Rank 73.
Scale-Type
SR
F-SR.
SPL
F-SPLJ
OSR
F-OSR.
24,24
192,192
all
69
11
62
18
71
9
24,24
64,64
sos
66
11
60
68
9
8,8
192,192
eos
68
11
61
18
71
9
8,8
64,64
equal
65
11
58
18
68
Table 13: The Analysis Results for TuKA Parameter Growth for Scaling of Ranks (scale factors 4).
Rank 11
Rank 73, F1
Scale-Type
SR
F-SRI
SPL1
F-SPL
OSR
F-OSR,
32,32
256,256
all
69
11
62
18
71
9
32,32
64,64
66
11
60
18
68
9
8,8
256,256
eos
68
11
61
71
9
8,8
64,64
equal
65
58
68
9
H DOES THE THIRD-ORDER TENSOR CAN WELL REPRESENT THE MULTT-HIERARCIIAL KNOWLEDGE?
Our TuKA uses fourth-order tensor to represent multi-hierarchical navigation knowledge (i.e., scenes and environments). In this section, we explore whether third-order tensors are sufficient to represent multi-hierarchicl knowledge. Specifically, a three-order tensor X X can be decomposed:
X X U X2 2
X3 U
(22)
where Xn, = 2, 4 denotes the n-th modal product of tensor matrix. G R Xraxxs is core tensor, which contains interaction information between all patterns, and is used to learn the navigation-shared knowledge. The factor matrix U E Ra represents the transformation pattern of the feature from dimension to which can be regarded represents the transformation pattern of the feature from b dimension to r2, which can be regarded as a encoder. The factor matrix 3 E (RM X represents the (M x N) group experts, with each scene expert ( is used to learn the 1-th specific navigation task knowledge. Thus, for the t-th scenario with s-th scene and e-th environment adaptation, we extract the task-specific 3 ] from the tensor X' to constitute adaptation weight A W,:
decoder; U E R Xra
AW, = U (9 Xa (L,,)))
(23)
The results learning multi-hierarchier knowledge using third-order tensors are presented Table Based on the experiment results, compared to the nuuti-hierarchical knowledge decoupled rep- resentation of fourth-order tensors, third-order tensors do not decouple the multi-hierarchicall knowl- edge. Instead, they employ single expert matrix to learn all knowledge sequentially, consequently yielding suboptimal performance. In summary, the proposed multi-hierarchiie representation learn- ing using higher-order tensors is significant.
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Table 14: The Analysis Results (SREE-SRSSL/F-SPPOSSFFFOO in %) Third-order Tensors for Representation multi huuti-hierrarchiaal Knowledge.
Comparisons
T1
T2
T3
T5
T6
T8
T9
T10
T11
T12
T13
T14
T15
T16
T17
T18
T19
T20
Avg.
3-order SR
65
21
49
61
30
46
70
30
70
71
60
61
41
73
54
71
48
49
35
68
54
4-order SR
79
23
71
81
33
50
87
38
79
75
79
67
50
86
71
76
67
63
43
81
65
3-order F-SR +
36
33
15
11
2
36
33
16
4-order F-SR |
27
23
21
1
28
9
0
30
24
10
4
21
0
11
3-order 1
16
51
43
60
65
51
38
48
62
42
51
48
4-order SPL. 1
20
67
73
25
32
34
73
71
56
48
65
70
60
67
58
3-order F-SPI.
18
36
38
21
36
18
45
26
15
18
22
4.3
39
24
35
16
39
26
4-order F-SPL
28
38
29
3-order OSR
71
22
55
66
36
51
72
26
74
71
66
61
42
80
51
70
51
53
41
74
57
4-order OSR
81
26
73
81
57
88
38
83
79
83
71
52
74
78
71
63
84
68
3-order F-OSR
12
38
35
14
35
12
39
15
2
10
35
38
14
26
15
36
0
19
4-order F-OSR
24
19
18
28
8
2
4
27
19
8
3
20
0
9
I DOES THE HIERARCHICAL ARCHITECTURE PLAY A ROLE, OR IS IT THE HIGHER-ORDER TENSORS?
Our proposed TuKA represents task adaptation in higher-order tensor space, which naturally de- couples scene and environment knowledge. A natural question is whether such gains stem merely from having hierarchical architecture (scene branch environment branch), from the expressive power of high-order tensor representations. To disentangle these factors, we design hierarchical baseline using three-layer layer LoRA modules. Specifically. construct three-level LoRA adaptation C. B, A.
A AW =
A shared matrix A that captures common knowledge across all tasks.
Multiple scene-specific matrices B., each dedicated to a scene s.
Multiple environment--pecific matrices C., each dedicated an environment e.
During training, the model activates one B, and one C. depending on the current task, while A remains shared. The A is constrained to employ loss function similar to elastic weight consolida- tion 6 and B., C. is constrained to employ a loss function similar to expert consistency constraints (Eq 7 and task-specific expert subspace orthogonal constraint (Eq(8). employs se- lection inference based on the proposed expert selection methodology ($ 3.4). This design mimics scene-environment hierarchy but still operates entirely within second-order (matrix) structures, without using higher-order tensor decomposition. We align the parameter budget of this hierarchi- cal LoRA with TuKA's default configuration for fair comparison (keeping the rank of E Rax18 B 488x48 and C 48xb that the total trainable parameters comparable). Specifically, we use one A, five B., and four C., thus
Paramg LORA = 1024 48
48 48
1024 257, 280 R 0.3M.
The training follows the AML-VLN setting (20 sequential tasks, same task order, optimizer, and hyperparameters). We report averaged SR. F-SR. SPL, F-SPL, OSR, and F-OSR across the 20 tasks. The results are summarized in Figure IT and Table 15 Based on results, the hierarchical ABC-LoRA improves over flat LoRA baselines by better capturing scene environment structure, showing modest gains in SR and reduced forgetting compared to O-LoRA. However, it consistently under- performs TuKA. While hierarchical LoRA introduces structural disentanglement, it cannot fully capture the multi-hun hiitiiihierarhicall interactions (scene envirommet couplings) that higher-order tensors naturally model. Specifically, TuKA's Tucker enables parameter sharing not only across scene or environment dimensions individually, but also across their joint combinations, which hierarchical LoRA lacks.
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1000
Teme
(a) The analysis results with SR
(b) The analysis results with SPL
(c) The analysis results with OSR
1
(d) The analysis results with F-SR
(e) analysis results with F-SPL
The analysis results with F-OSR
Figure 11: The comparison results for ABC-LoRA with hierarchical architecture.
Table 15: The Comparison Results (SKFFSSRSSLLAASPLSSSRRFOOG in %) of ABC-LoRA for Representation Multi-hierarchiin Knowledge.
Comparisons
T1
T2
T3
T11
T12
T13
T14
TIS
T17
T18
T20
Avg.
O-LoRA SR
67
19
47
58
31
42
67
27
67
62
58
49
38
68
52
62
46
52
34
71
51
ABC-LoRA SR
71
20
55
64
52
72
61
52
Allday Walker
79
23
71
67
71
63
O-LoRA F-SR J.
19
41
41
28
38
24
38
1.5
17
11
13
43
2
40
24
32
5
35
24
ABC-LORA F-SR
15
36
33
21
30
1.5
33
26
5
25
AlldayWalker F-SR
2
27
23
-3
24
O-LoRA SPL
64
17
47
55
28
41
66
27
58
61
49
38
35
59
46
58
41
47
28
57
46
ABC-LoRA SPL
66
17
50
60
28
41
66
27
58
60
52
50
45
70
55
61
50
49
31
61
50
Walker SPL
70
20
67
73
68
56
75
65
62
60
O-LoRA F-SPL.L
25
44
43
28
38
33
26
17
46
29
25
35
ABC-LORA F-SPL.
16
35
25
21
29
15
15
38
21
1.5
18
21
42
11
31
22
32
1.5
31
23
AlldayWalker Walker F-SPL.
13
31
28
25
12
34
15
12
14
29
14
8
27
O-LoRA OSR f
71
20
47
58
74
57
73
54
55
ABC-LoRA OSR f
75
25
59
70
35
45
69
35
75
68
62
60
45
76
68
71
60
60
49
811
59
AlldayWalker Walker OSR
81
26
73
81
41
57
88
38
83
79
83
71
52
87
74
78
71
633
48
84
68
O-LoRA F-OSR 1.
17
39
14
37
32
ABC-LORA F-OSR
8
36
35
20
30
10
8
6
11
25
21
20
3
20
Allday Walker F-OSR
24
19
8
18
28
8
-2
4
27
-
19
-1
3
20
J How DOES SCALABILITY FARE WHEN KNOWLEDGE EXHIBITS A MORE MULTI-HIERARCHICAL STRUCTURE?
Our proposed Allday Walker employs a fourth-order tensor Tucker decomposition to adapt to new navigation scenarios. Under settings, each navigation scenario comprises two hierarchies of
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Inputs
Shared Knowledge
Five-Order Tensor Adapter
Specific Expert
Specific Expert
Specific Expert
LLM :
Encoder
U1
FFN
Atteentin
2
FFN Attention
U3
U5
U2
1
Decoders
Outputs
Figure 12: Illustration the fifth-order tensor TuKA architecture.
knowledge: the scene and the environment. We explore how incorporating more hierarchical knowl- edge enables rapid adaptation using higher-order TuKA. Specifically, we add an additional hier- archies of knowledge, i.c. language instructions, following Dialogue Understanding Navigation
(DUN) (Thomason et al. (2020). We add one more hierarchies on top of the fourth-order tensor to form fifth-order tensor X' RaxxxMxNxP and it can be decomposed into:
X -
X2 U X3 U X4 U X5 U
(24)
where X 1,2,3, 4,5 denotes the n-th modal product of the tensor and matrix. Rri xraxrrxaaaaxx is core tensor, which contains interaction information between all patterns, and used to learn the navigation-she shared knowledge. The factor matrix U Raxr represents the transformation pattern of the feature from 71 dimension to a, which can be regarded as dimension- up matrix; 2 E R represents the transformation pattern of the feature from dimension r2, which can be regarded as dimension-redunn
matrix. The factor matrix U E R Mrs represents the .M group of scene experts, with each scene expert ( :] is used to learn the i-th specific scene knowledge; U RR- represents N group of environment experts, with each expert U* : used to learn the j-th specific environment knowledge. U E Rxrs represents group struction experts, with each expert [$ is used to learn the q-th specific instruction knowledge. The fifth-order tensor TuKA is shown in Figure T2 Thus, for the 1-th scenario with 8-th scene, e-th environment, and g-th instruction adaptation, and U q. from the high-order tensor X' to constitute weight AW;
extract the task-specific matrices U ( U
AW, = U (g X3 U [,):] X4 [e, X5 (p):)) (US)T.
(25)
For verification of the proposed fifth-order TuKA, construct three hierarchies knowledge bench- mark containing ten tasks, as shown in Table [6 and train our TuKA on it. The experimental results summarized in Table [6 Based on experimental results, our TuKA remains effective when ex- tended to a fifth-order tensor, demonstrating superior performance.
K DETAILED COMPARISON RESULTS
For ease of presentation, the data in Figure is presented after normalization. The original compari- results (SPL, F-SPL, OSR, F-OSR) for 7 are summarized in Table Table 19 Table 20 and Table 21 Due to the complexity of trajectory computation in real-world scenes and we focus more on task success rates on real-world, we only report results from simulated scenes -T))) In addition, we summarize the performance and parameter numbers of all comparison methods, as shown in Table 17 Based on the results, our Allday Walker achieves consistent superiority across various metrics.
L DOES LEARNING FOR MORE TASKS LEAD TO FORGETTING?
Our 24 tasks already cover diverse scenes and four distinct imaging conditions across both simula- tion and real- weal-world environments, forming a sufficiently challenging lifelong learning benchmark. To further validate the continual learning performance of Allday Walker when dealing with more
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Table 16: Statistics of the 10 Sequential Tasks for Verifying Fifth-order Tensor TuKA, and Some Comparison Results (SR/F-SR in %6..
Task Statistics
HydraLoRA
SD-LORA
Aalkey
Task ID
Scene ID
Environment
Instruction
SR
F-SR.
SR
F-SR
SR
F-SR.
Tc267MWGi7TT
Normal
VIN
46
44
69
13
79
SLpN33DmAk7
Low-light
DUN
60
41
35
51
29
ac262MwG7al
Overexposure
VLN
68
29
34
39
59hMv5qa7GM
Scattering
DUN
51
32
21
15
SLpN33DmAK7
Normal
VLN
28
8
87
ac262MwG7aT
Low-light
DUN
19
S9hNv5qa7GM
Overexposure
VLN
48
38
ac26ZMwG7aT
Scattering
DUN
52
SLpN3gDmAK7
Overexposure
VLN
79
40
SLpN3gDmAk7
Scattering
DUN
65
42
Avg
29
53
45
26
55
16
Table 17: Comparison of Baselines: Trainable Parameters and Averaged Metries on the 24-task
AML-VLN Benchmark Learning. Metrics (SR/F-SR in %) Are Averages across 24 Tasks, and
Metrics (SPL/F- SEL/F-SPLLOSRRF--O -SRR %) are Averages across First 20 Tasks Reported in the Paper.
Method
Avg SR
Avg F-SR
Avg SPL
Avg F-SPL
Arg OSK
Avg F-OSR
Parameter
Seq-FT
11
87
4
95
6
91
14.68 M
LwF-LoRA
12
84
92
9
86
14.68 M
EWC-LoRA
15
79
87
13
81
14.68 M
Dense MoLE
72
18
16.52 M
Sparse MoLE
62
MoLA
55
16.52 M
HydraLoRA
46
47
17.20 M
BranchLoRA
44
36
43
45
36
17.20 M
O-LoRA
52
23
46
31
54
22
16,52 M
SD-LoRA
56
18
50
25
58
17
16.52 M
AlldayWalker
65
11
58
18
68
15.64 M
Table 18: Test Results (SPL t in %) of Comparison Experiments under the AMI-VLLN Settings.
Comparisons
TI
T2
T3
TS
T7
T8
TI0
TII
TI2
TI3
T14
TIS
TI6
T17
TI8
T19
T20
Avg.
Seq-FT
0
0
0
4
0
0
8
5
4
12
2
18
TwF-LoRA
0
0
12
14
3
EWC-LoRA
6
8
2
4
7
4
10
12
14
8
10
15
14
11
5
23
Dense MoLE
5
11
16
14
15
10
17
18
9
Sparse MoLE
24
7
24
15
17
14
27
9
31
22
22
21
17
21
21
29
28
10
12
33
21
MoLA
8
36
26
24
36
14
HydraLoRA
45
11
39
39
20
31
51
18
52
35
29
29
27
34
29
41
33
24
15
48
33
BranchLoRA
13
24
20
41
41
33
52
24
O-LoRA
17
47
55
28
41
66
27
58
61
49
38
35
46
58
41
47
28
57
SD-LoRA
62
19
52
30
45
71
28
62
68
52
52
39
64
50
65
49
45
36
53
50
Allday Walker
70
20
67
73
25
32
80
34
73
71
68
56
48
75
65
70
62
60
41
67
58
lasks. We also conduct additional continual learning experiments by adding two new real-world tasks and four simulation tasks. For additional task scenes and environments, refer to Table 22 The results are summarized in Table The results show that incorporating more tasks does not lead to noticeable performance degradation. demonstrating that AlldayWalker Walker remains stable under even larger- scale lifelong learning settings. The visualization of these navigation tasks shown in
Figures 13 14
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Table 19: Test Results (F-SPL
%) of Comparison Experiments under the AML-VLN Settings.
Comparisons
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
T15
T16
T17
T18
T19
T20
Avg.
Seq-FT
100
100
100
100
100
95
94
100
LwF-LoRA
94
100
94
100
100
100
92
93
90
94
95
86
88
85
92
87
88
100
79
92
EWC-LoRA
91
93
89
90
94
92
88
93
87
85
92
83
93
94
89
80
79
84
94
75
87
Dense MoLE
78
78
89
82
69
67
86
68
Sparse MoLE
76
64
85
58
67
57
53
58
85
56
70
MoLA
68
81
63
62
64
65
48
80
48
67
59
49
69
49
72
62
54
69
83
47
63
HydraLoRA
51
67
55
49
64
46
68
46
50
52
44
66
46
69
56
49
53
75
33
54
BranchLoRA
32
57
32
27
45
58
O-LoRA
42
27
38
21
17
29
41
44
SD-LoRA
17
36
35
19
37
16
16
43
25
11
16
21
42
10
38
23
36
15
39
10
25
Allkay
12
25
8
12
34
15
12
5
16
15
28
18
Table 20: Test Results (OSR
%) of Comparison Experiments under the AMI-VIN Settings.
Comparisons
TI
T2
T3
TS
T7
T8
TIO
TII
T12
TI3
T14
TIS
TI6
T17
TI8
T19
T20
Avg.
Seq-FT
5
0
0
0
5
0
10
5
10
8
12
7
14
8
6
24
TwF-LoRA
8
0
4
17
15
15
12
7
EWC-LoRA
14
10
13
3
12
12
4
23
14
13
19
11
13
14
19
16
17
9
24
13
Dense MoLE
11
15
18
19
13
20
10
Sparse MoLE
29
13
27
33
18
28
31
11
59
24
23
30
20
29
25
36
31
10
14
38
26
MoLA
12
22
34
26
31
14
HydraLoRA
48
16
39
46
17
37
57
18
58
39
33
38
29
43
33
42
38
28
20
52
37
BranchLoRA
16
26
25
48
42
58
24
O-LoRA
71
20
47
58
33
68
28
74
76
58
57
39
73
54
62
46
55
37
71
SD-LoRA
72
23
57
69
37
52
75
28
75
74
67
62
43
81
56
72
52
54
43
73
58
Allday Walker
81
26
73
81
41
57
88
38
83
79
83
71
52
87
74
78
71
63
48
84
68
Table 21: Test Results (F-OSR J in %) of Comparison Experiments under the AML-VLN Settings.
Comparisons
T1
T2
T3
T4
T5
T6
T7
T8
T9
TIO
TII
T12
T13
T14
T15
T16
T17
T18
T19
T20
Avg.
Seq-FT
100
91
100
100
100
94
93
100
93
94
83
89
92
83
89
73
LwF-LoRA
92
92
88
89
100
90
92
89
77
91
91
77
88
78
87
79
82
80
94
69
86
EWC-LORA
81
94
84
73
77
71
72
Dense MoLE
73
90
70
71
77
81
76
78
72
73
61
79
72
69
61
Sparse MoLE
68
84
67
50
72
49
53
82
55
68
64
56
67
46
70
57
51
82
82
54
64
MoLA
79
45
63
46
61
41
39
54
60
44
HydraLoRA
39
62
32
58
37
25
45
49
34
40
50
49
29
BranchLoRRA
34
55
52
27
53
28
12
49
27
48
26
22
58
15
52
28
35
37
56
11
36
O-LoRA
39
17
37
4
24
SD-LoRA
36
12
33
10
3
36
2
-4
12
11
Allday Walker
24
19
8
18
-1
28
2
27
-5
19
8
4
-7
Table 22: Descriptions and dataset statisties of added Tasks T25-T30.
Task
Scene Description
Environment
Train Number
Test Number
T25
8WUmhLawe2A
Normal Environment
460
120
T26
awu2ALawe2A
Low-light Environment
460
120
T27
8WUmhLawe2A
Scattering Environment
460
120
T28
Lww22A
Overexposed Environment
460
120
T29
Real-World Scene
Normal Environment
400
100
T30
Real-World Scene
Low-light Environment
400
100
30

---

Published as a conference paper at ICLR 2026
VLN instruction: Take around the Walk the bathroom take walk down the hallway. the left. room past chair, Walk onto the patio
T25
user instruction: Walk through doorway next to the white art piece and hallway. first left in hallway toward the bathroom, but stop outside the closet doorway on left.
T26
VLN user instruction: Walk out the bathroom. Turn right at the end of the hall. Walk through the doorway and into the room entrance to the left. Stop in the entranoe to the room to the left.
T27
n
VLN instruction: Walk the When you gel the the slains, walk straight ahead and go Uhrough the T28 doorway painting hanging This bedroom. Stop when you enter bedroom.
Obsenvrbre
Singulator
Tack
Figure 13: Illustration of the visualization examples for T25-T28.
VLN user instruction: Proceed straight ahead until you reach the blue cone-shaped barrel, tum right. Continue until see the yellow cone-shaped barrel, then stop at the small yellow ball positioned against the wall.
T30
Pheeraatio Orservalors
Fiful State
Low-Light
End State
VLN user instruction: Proceed forward to the space between the two yellow cone-shaped barrels, turn left and walk to the red cone-shaped barrel, turn left again, beside the small ball against wall.
Intiial State
Normal-Light Progress
End State
Figure 14: Illustration of the visualization examples for T29-T30.
M DISCUSSIONS AND FUTURE WORKS
Our work introduces Tucker Adaptation and decoupled knowledge incremental learning for lifelong VLN, While the results are encouraging, several interesting directions remain. An interesting direction to combine TuKA with memory-based or surreval-auumented architec- tures, which may further enhance long-term retention and reduce catastrophic forgetting. Further- more, beyond VLN, the idea of high-order adaptation could generalize to other embodied tasks such manipulation, dialogue-based navigation, multimodal planning.
Future Works:
31

---

Published as a conference paper at ICLR 2026
VLN user instruction: Go down the hallway and continue through the arches.l Go passed the table and to the right and then continue forward until you reach the back the brown couch.
G1
VLN instruction: Exit nice closel and loft.
Then enter the bedroom.
Stand next the nightstand.
G2
Oloeraation
user instruction:Exl bedroom, down hallway past cabinel, top stairs.
G3
VLN user instruction: Facing the table ço left and the right around table and go through archway, turn right, G4
the intersection take a right and then go into the archway toward the sink and stop.
Iritial Etato
Simulator
End State
Figure 15: Illustration of the visualization examples for GI-G4.
VLN user instruction: Proceed forward to the yellow cone-shaped barrel, then turn right until you see the red extinguisher. Continue forward and stop front of the fire extinguisher.
Initia
Normal- Task Progress
VLN instruction: Please proceed around the the red ball, continuing straight ahead until you see traffic cone. Turn left and stop front of the water cup.
Task Progress
Figure 16: Illustration of the visualization examples for G5-G6.
Societal Impacts: This work aims to advance embodied agents that can operate in diverse conditions applications in service robotics, accessibility, and
with less retraining, which may support disaster response. However, navigation agents also raise concerns. Privacy and security issues may arise when deploying agents homes or sensitive environments. Moreover, biases in simulated data or task design could lead to unfair performance across different cultural environmental contexts. We encourage future work to evaluate not only technical performance but also ethical and societal implications before deployment.
32


Reviews:
[{"review_id": "RwY9EHCHL9", "reviewer": "Reviewer_W2Xo", "summary": "This work focus on all-day long multi-scenes lifelong learning for vision-language-navigation. The authors collected 24 scenes including different buildings and using different light intensity to simulate day and night, the author also scattered the environments using a scattering model. This work proposes a continual learning method in order to learn new knowledge without catastrophic forgetting. The method leverages tensor Tucker decomposition to decouple a high-order tensor for multi-hierarchical knowledge. Besides, this work also proposed a decoupled knowledge incremental learning strategy to consolidate shared subspaces while constraining the task-specific knowledge. This work conducts experiments on the collected 24 scenes and compared with different LoRA-based continual learning methods and achieves the best performance.", "questions": "1.\tHow are the intensities of scattering, low-light, and overexposure controlled to ensure that these environments are visually distinct yet not overly degraded to disturb visual observation?\n2.\tCould the authors provide visual examples of the observations under different the three conditions to illustrate this distinction?\n3.\tAdditionally, do cases exist where two environments within the same scene differ only slightly in illumination? If so, are they still treated as separate tasks, and could such overlap potentially weaken the challenge of continual learning?\n4.\tDuring inference, TuKA retrieves the appropriate scene and environment experts by matching CLIP features via cosine similarity. How robust is this retrieval mechanism when the test scene partially resembles multiple training scenes in texture or layout? Has the paper evaluated how retrieval ambiguity or mismatched expert selection affects overall navigation performance?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1.\tThis article explores continual learning in vision-language navigation which is novel and important for future research.\n2.\tThe authors represent multi-hierarchical knowledge as high-order tensor and proposes an effective method to decouple task-shared and task-specific representations. The method could also successfully learn new task-specific knowledge incrementally without catastrophic forgetting.\n3.\tExtensive experiments are conducted by the authors which reveals the effectiveness of their method.", "weaknesses": "1.\tThe two critical challenges presented in the introduction (Lines 75–78) appear very similar, which may cause confusion for readers. The authors are encouraged to clarify the distinction between them.\n2.\tThe authors collected only 24 scenes for the multi-scene lifelong VLN benchmark setting. Adding more scenes would make the results more convincing; otherwise, the authors should provide justification for why these 24 scenes are sufficient (such as the number of tested episodes).\n3.\tThis work is based solely on the StreamVLN agent. It would be preferable if the authors could conduct additional experiments using other baseline agents to better demonstrate the general effectiveness of the proposed continual learning method.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "mvsXKXzR1a", "reviewer": "Reviewer_edmj", "summary": "This paper proposes an interesting idea of a multi-scene lifelong learning approach for Vision-and-Language Navigation (VLN) tasks. The authors introduce Tucker Adaptation (TuKA), a parameter-efficient method that represents multi-hierarchical knowledge as a high-order tensor and applies Tucker decomposition to decouple task-shared and task-specific knowledge.\nThey further propose a decoupled knowledge incremental learning strategy to support continual learning of multi-hierarchical knowledge.\nBased on TuKA, the authors develop a lifelong VLN agent, AlldayWalker, which achieves improved navigation performance compared to LoRA-like approaches on the modified VLN benchmark, demonstrating the value of third-order tensor adaptation for continual representation learning for VLN tasks.", "questions": "Please refer to the weakness section where I listed questions as well.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* The paper introduces a parameter-efficient adaptation method (TuKA) that leverages Tucker decomposition to decouple and represent multi-hierarchical knowledge in a high-order tensor, enabling more expressive representation learning.\n\n* The integration of TuKA into a lifelong VLN agent (AlldayWalker) demonstrates the potential of high-order tensor adaptation for continual learning in navigation tasks.\n\n* The authors make meaningful modifications to existing VLN simulators, allowing more systematic evaluation of adaptation performance under diverse out-of-distribution conditions.", "weaknesses": "* Line 76: The challenges (i) and (ii) appear to be repeated. Please clarify or consolidate to avoid redundancy.\n\n* The proposed method appears to directly apply Tucker decomposition to the VLN task. The paper should more clearly articulate what unique ideas or design choices provide additional contributions beyond standard Tucker-based factorization.\n\n* The paper omits relevant discussions of recent test-time adaptation approaches for VLN, such as FSTTA (ICML 2024) and FeedTTA (ICML 2025). In particular, FeedTTA also investigates catastrophic forgetting issues in adaptation, which seems closely related to the current work. A comparison or at least a discussion of similarities and differences would help clarify the contribution.\n\n* The authors state that VLN knowledge can be decomposed into navigation skills, scene-specific, and environment-specific knowledge, whereas MoE-LoRA (HydraLoRA) cannot handle more than two-dimensional hierarchies. However, MoE-like methods can, in principle, combine multiple specific experts across more than two dimensions. It is unclear whether it is correct to categorize MoE-LoRA (Eq. 1) as strictly “two-dimensional,” since it composes K experts with potentially different feature dimensionalities. As the main contribution hinges on representational advantages over existing methods, this aspect should be clarified more rigorously.\n\n* Eq. (2) shows the intended decomposition of VLN tasks into shared encoders, scene knowledge, and environment knowledge. However, it is unclear how this decomposition is actually enforced during adaptation learning.\nThe conceptual difference between Eq. (1) and Eq. (2) seems to be in the additive or multiplicative composition of the experts (task-specific knowledge). The authors should provide stronger justification or empirical support demonstrating that Eq. (2) yields better representational capacity than Eq. (1).\n\n* The results in Table 3 are difficult to interpret in terms of the effectiveness of the proposed components. For instance, adding U1 or U2 does not necessarily improve SR, and there is no baseline result for “Sd-G only,” which makes it harder to assess the contribution of U1 and U2.\n\t​\n* Table 4 does not clearly specify which tasks or scenarios the agent was trained and tested on.\nIt remains unclear how these results validate that L_ewc and L_co effectively prevent catastrophic forgetting.\nThe test settings and interpretation of the results should be described in more detail.\n\n\nI would currently lean toward a borderline reject, but I would be open to increasing the rating if the rebuttal provides clarifications on the questions and concerns.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "CHUR2hsyQN", "reviewer": "Reviewer_Xr3z", "summary": "This paper addresses the continual learning problem in Vision-and-Language Navigation (VLN) by formalizing it as the All-Day Multi-Scenes Lifelong VLN (AML-VLN) problem. It proposes Tucker Adaptation (TuKA) — a parameter-efficient, multi-hierarchical adapter that represents navigation knowledge as a high-order tensor. By leveraging Tucker decomposition, TuKA disentangles shared, scene-specific, and environment-specific subspaces, enabling continual adaptation with reduced interference across sequential VLN tasks. The method is integrated into the AlldayWalker agent, which employs a decoupled knowledge incremental learning strategy to preserve prior knowledge while adapting to new conditions.", "questions": "1.\tCould you include experiments in completely unseen environments?\n2.\tCould you discuss how much TuKA’s performance might depend on the training sequence?\n3.\tCould you report how the base StreamVLN policy performs under the same AML-VLN setup? Since TuKA is built upon StreamVLN, this comparison would clarify  how much improvement TuKA provides over the baseline model itself.\n4.\tAs new environments or scenes are introduced, the number of factors would also increase — does this lead to improved generalization performance?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe paper is well-written and clearly organized, with a solid motivation and clear presentation of the continual learning setting in VLN.\n2.\tAddresses the underexplored problem of continual learning in vision-and-language navigation — a task that naturally involves sequential adaptation yet has received limited attention in prior research.\n3.\tProposes TuKA, a Tucker-based adapter that decouples scene- and environment-specific experts. This structure enables task-wise adaptation while mitigating interference, effectively reducing catastrophic forgetting.\n4.\tProvides comprehensive experiments, including ablations, scalability analyses and comparisons with multiple adapter-based baselines.\n5.\tEnables LLM-based embodied agents to perform continual learning, achieving higher SR/SPL and lower forgetting rates than other continual learning frameworks.", "weaknesses": "1.\tThe current experiments appear to evaluate tasks within the same building. Since a central goal of continual learning is to enable agents to transfer previously acquired knowledge to unseen environments, it would be valuable to include evaluations in completely new buildings or environments to more clearly demonstrate TuKA’s generalization ability beyond the trained domain.\n2.\tContinual learning results can be sensitive to the order in which tasks are learned. It would be helpful to clarify whether you have experimented with different training orders — for example, by shuffling or reversing the task sequence — to examine how such variations affect TuKA’s performance and forgetting behavior.\n3.\tTuKA structurally separates experts into scene- and environment-specific components, but it would be helpful to include experiments verifying whether each expert actually learns the characteristics of its corresponding task.\n4.\tIn L806–807, it is mentioned that “they often suffer from catastrophic forgetting of previously learned scenarios, severely limiting their robustness in dynamic real-world settings.” Adding a discussion on Test-Time Adaptation (TTA) [1-2] as one possible way to address this issue would make the paper more complete.\n5.\tI will consider raising the score if these points are clarified.\n[1] Gao, et al., \"Fast-Slow Test-Time Adaptation for Online Vision-and-Language Navigation.\", ICML 2024.\n[2] Kim, et al., \"Test-Time Adaptation for Online Vision-Language Navigation with Feedback-based Reinforcement Learning.\", ICML 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7bsLpSfv5D", "reviewer": "Reviewer_gySm", "summary": "This paper formulates the All-Day Multi-Scenes Lifelong VLN (AML-VLN) problem, that is, agents learn sequential scenarios without forgetting. The authors propose: 1) TuKA (high-order tensor adapter) to model multi-hierarchical knowledge; 2) DKIL (incremental learning strategy) to mitigate forgetting. Experiments on the Allday-Habitat benchmark (24 tasks) show AlldayWalker outperforms LoRA variants.", "questions": "See Weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Formalizing AML-VLN clarifies the \"scene-environment-lifelong\" gap in VLN research.\n2. Allday-Habitat provides a unified testbed for lifelong VLN, saving future work from benchmark construction.\n3. Experiments cover diverse baselines (10 LoRA variants) and key ablation (tensor order, DKIL losses), ensuring basic reliability.", "weaknesses": "1. novelty is limited: TuKA repurposes Tucker decomposition (a classic technique) for VLN adapters—there is no novel mathematical insight (e.g., improved decomposition efficiency, dynamic rank adjustment). DKIL’s \"decoupled learning\" is a standard combination of Elastic Weight Consolidation loss and orthogonality loss, with no new mechanism to handle cross-scene/environment knowledge transfer.\n\n2. Experiment design is incomplete: \n(1) No generalization test to unseen scenes/environments (critical for lifelong learning and VLN task)—the paper only tests sequential tasks from pre-defined sets.\n(2) No ablation of \"tensor vs. matrix MoE\": A direct comparison between TuKA and a matrix-based MoE with the same hierarchy (scene+environment branches) would confirm if tensors add unique value.\n(3) Real-world deployment is superficial: The DeepRobotDog experiment lacks details (e.g., navigation success rate in real low-light vs. simulation, failure cases).\n\n3. Practicality is unproven: No inference latency/memory data: High-order tensor operations may be too slow for real-time robot navigation (e.g., edge devices), but the paper ignores this.\n\n4. Some related and important works are missing citations:\n[1] NavQ: Learning a Q-Model for Foresighted Vision-and-Language Navigation\n[2] Test-time Adaptive Vision-and-Language Navigation\n[3] Learning Vision-and-Language Navigation from YouTube Videos\n[4] Magic: Meta-ability guided interactive chain-of-distillation for effective-and-efficient vision-and-language navigation", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)