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Nav-CoT-110K: Multimodal Reasoning Dataset

Updated 18 September 2025
  • The paper introduces Nav-CoT-110K, a dataset presenting 110K structured multimodal chain-of-thought examples to decompose complex decision-making in occlusion and navigation tasks.
  • It leverages controlled synthetic data generation with template-enforced annotations to provide detailed stepwise supervision and robust 3D-aware reasoning.
  • The dataset significantly improves model performance in both occluded object recognition and embodied navigation by offering interpretable, intermediate outputs for effective training.

Nav-CoT-110K is a large-scale, synthetic, chain-of-thought (CoT) reasoning dataset constructed to scaffold robust multimodal reasoning and navigation for embodied AI and occluded object recognition. It comprises approximately 110,000 high-quality multimodal step-by-step CoT examples, with each instance designed to provide explicit, structured decomposition of complex decision-making processes in the context of either handheld occluded object identification or embodied navigation in 3D environments. Nav-CoT-110K plays a dual foundational role in two major research efforts: multi-stage occlusion recognition under 3D-aware supervision, and reasoning-guided navigation for embodied agents. It is distinct in its scale, structure, and explicit formulation of multi-stage reasoning grounded in real and synthetic visual contexts.

1. Dataset Structure and Contents

Nav-CoT-110K consists of structured, multimodal examples, where each sample is organized as follows:

  • Visual Modality: Depending on its application context, inputs are either RGB images depicting occluded, hand-held objects (notably from synthetic sources such as the ObMan dataset), or egocentric RGB-D views and point cloud observations from public 3D scene datasets (e.g., Matterport3D, HM3D) for navigation.
  • Instructional/Task Modality: Free-form or benchmark-sourced natural language instructions. For navigation, these are drawn from embodied AI benchmarks (e.g., R2R, R2R-CE, RxR-CE for instruction-following; SOON/OVON for object-goal tasks).
  • Chain-of-Thought Annotation:
    • For occluded object recognition, annotations form a multi-stage chain:
    • 1. Description Stage: Enumeration of low-level attributes (e.g., "Is the object round?").
    • 2. Self-Reflection Stage: Assessment query regarding identifiability from current evidence.
    • 3. Final Decision Stage: Explicit object identification.
    • For embodied navigation, CoT traces are demarcated using template tags (e.g., > reasoning, <action>decision/action</action>), sequentially articulating perception, semantic interpretation, and action planning.
  • Scene/Instance Metadata: Unique identifiers allowing cross-referencing to the 3D environments or object contexts from which data are sourced.

The initial pool for navigation-oriented data was approximately 115,000 generated examples; a two-stage filtering pipeline ensures consistency, completeness, and logical correctness, yielding the final 110K sample corpus (Liu et al., 13 Sep 2025, Wang et al., 7 Apr 2025).

2. Data Generation, Annotation, and Filtering

Nav-CoT-110K leverages controlled synthetic generation and annotation strategies tailored to its two principal domains:

Occluded Object Recognition

  • Images utilize the ObMan synthetic dataset, affording precise manipulation of occlusion by hand poses.
  • Annotation is performed with structured query sets, guiding annotators (and/or semi-automated processes) through successive questions decomposing recognition into discrete stages.
  • The design of multi-stage annotation ensures both label quality (fine-grained, interpretable) and dataset consistency, supporting models that require step-by-step supervision.

Embodied Navigation

  • Scene and action data originate from Matterport3D, HM3D, and associated instruction benchmarks.
  • CoT traces are produced by prompting a vision-LLM (Gemini 2.5 Pro) with composite prompts: navigation instruction, current egocentric visual input, feasible actions, and strict output templates (<think>/<action> tags).
  • Filtering is implemented in two phases: rule-based discarding of incomplete or inconsistent outputs, and cross-verification of action feasibility relative to the scene and instruction.

The resulting dataset reflects an unusual combination of detail, structure, and cross-modal consistency, positioning it as a reference standard for CoT-grounded reasoning research.

3. Integration of 3D-Aware Supervision and Reasoning

Nav-CoT-110K introduces 3D-aware supervision as a core innovation, particularly in the context of occluded object comprehension:

  • 3D Reconstruction Expert Model: Each sample is augmented by predictions from a model trained to reconstruct full object shapes from occluded RGB inputs. This informs both dataset annotation and subsequent model consumption of 3D cues.
  • Supervision Signals: During training, the standard visual recognition loss is supplemented with 3D reconstruction loss (object shape from occluded input) and an auxiliary 2D occlusion map loss.

    L3D=E(I,So,Inovel)D[L3D(f3D(ISo;θ3D),Inovel)+λL2D(Γ(ISo;θΓ),M3D)]L_\mathrm{{3D}} = \mathbb{E}_{(I, S_o, I_\mathrm{{novel}}) \sim \mathcal{D}} \left[ L_\mathrm{{3D}}(f_\mathrm{{3D}}(I \odot S_o; \theta_\mathrm{{3D}}), I_\mathrm{{novel}}) + \lambda L_\mathrm{{2D}}(\Gamma(I \odot S_o; \theta_\Gamma), M_\mathrm{{3D}}) \right]

    where II is the input image, SoS_o the occlusion mask, InovelI_\mathrm{{novel}} novel view supervision, and M3DM_\mathrm{{3D}} the predicted 3D occlusion map.

  • Reasoning Integration: In chain-of-thought training pipelines, model predictions at each reasoning stage (attribute queries, self-reflection, final object decision) may condition explicitly on cues from 3D expert modules, representing a tight coupling of perceptual geometry and linguistic logic (Wang et al., 7 Apr 2025).

This setup ensures that stepwise explanatory traces are not divorced from robust perceptual features, but enriched by topologically and spatially aware signals.

4. Chain-of-Thought (CoT) Reasoning Methodologies

Chain-of-thought reasoning is the central organizing principle of Nav-CoT-110K:

  • For Occlusion Tasks: Supervised learning follows a three-level loss:

    minθSD,θSR,θFDE(xv,XSD,xtSR,xtFD,R3D)D[iαiLSD(fi(xv,xti;θi),yi)+λSRLSR(fSR(xv,xtSR,ASD;θSR),ySR)+λFDLFD(fFD(xv,xtFD,aSR,R3D;θFD),yFD)]\min_{\theta_\mathrm{{SD}}, \theta_\mathrm{{SR}}, \theta_\mathrm{{FD}}} \mathbb{E}_{(\mathbf{x}_v, \mathbf{X}_\mathrm{{SD}}, \mathbf{x}_{t_\mathrm{{SR}}}, \mathbf{x}_{t_\mathrm{{FD}}}, R_{3D}) \sim \mathcal{D}} \bigg[ \sum_i \alpha_i L_\mathrm{{SD}}(f_i(\mathbf{x}_v, \mathbf{x}_{t_i}; \theta_i), y_i) + \lambda_\mathrm{{SR}} L_\mathrm{{SR}}(f_\mathrm{{SR}}(\mathbf{x}_v, \mathbf{x}_{t_\mathrm{{SR}}}, A_\mathrm{{SD}}; \theta_\mathrm{{SR}}), y_\mathrm{{SR}}) + \lambda_\mathrm{{FD}} L_\mathrm{{FD}}(f_\mathrm{{FD}}(\mathbf{x}_v, \mathbf{x}_{t_\mathrm{{FD}}}, a_\mathrm{{SR}}, R_\mathrm{{3D}}; \theta_\mathrm{{FD}}), y_\mathrm{{FD}}) \bigg]

    with separate parameters and loss weights for description (θSD\theta_\mathrm{{SD}}), self-reflection (θSR\theta_\mathrm{{SR}}), and final decision (θFD\theta_\mathrm{{FD}}).

  • For Navigation Tasks: Each example produces a trace of form:
    1
    2
    
    <think>stepwise observation and inference</think>
    <action>discrete navigation command</action>
    These traces supervise sequence-to-sequence models to couple visual input, spatial reasoning, and final action choice in a chain, with explicit format enforcement.

The chain-of-thought approach yields two primary benefits: (1) more reliable, interpretable intermediate outputs, and (2) increased model generalization by decomposing global decisions into local, verifiable steps.

5. Evaluation Metrics and Quantitative Performance

Evaluation on Nav-CoT-110K is multifaceted:

  • Occlusion Recognition Metrics:
    • Description Score: Accuracy on low-level attribute queries.
    • Reflection Score: Reliability of self-assessment as to evidence sufficiency.
    • Decision Score: Final object classification accuracy after CoT evidence aggregation.
    • Integration of 3D supervision and CoT yields decision score improvements of 15.75%, 15.30%, 16.98%, 14.62%, 4.42%, 3.63%, 6.94%, and 10.70% (across two settings and several baselines), demonstrating the effectiveness of the approach (Wang et al., 7 Apr 2025).
  • Navigation and Reasoning Metrics:
    • Success Rate (SR) and Path Fidelity: Standard embodied navigation metrics.
    • Format Reward: Rformat=1R_\mathrm{{format}} = 1 if the output adheres to required tag templates, $0$ otherwise.
    • Navigation Reward: Rpath=exp(kDF(T,T^))R_\mathrm{{path}} = \exp(-k \cdot D_F(T, \hat{T})), with DFD_F the trajectory distance and kk a decay rate (Liu et al., 13 Sep 2025).
    • Empirical evaluation reveals >8% average improvement in reasoning and navigation performance for models initialized on Nav-CoT-110K, relative to strong prior baselines.

6. Role in Model Training and Deployment

Nav-CoT-110K underpins several key model training regimes:

  • Cold-Start Initialization: Initially training models on Nav-CoT-110K (prior to RL or fine-tuning) is critical for ensuring that subsequent reasoning traces are structurally and semantically coherent.
  • Reinforcement Learning with Structured Supervision: In navigation, models trained on Nav-CoT-110K are further optimized under GRPO (Group Relative Policy Optimization), combining format, understanding, and navigation rewards with a KL penalty.
  • Real-World Applications: Pretraining on Nav-CoT-110K improves the translation of semantic instructions into low-level control on actual mobile robots. Case studies using WHEELTEC R550 robots with Jetson Orin Nano confirm robust performance under computational constraints (Liu et al., 13 Sep 2025).

A plausible implication is that datasets embodying detailed CoT structure confer advantages both to pure perception models (by enabling staged supervision) and embodied agents (via cold-start reasoning).

7. Impact, Limitations, and Research Trajectory

Nav-CoT-110K advances the field of multimodal reasoning in several respects:

  • Breadth: By covering both object-centric (occluded recognition) and spatial (navigation) CoT applications, it enables cross-domain adaptation of reasoning methods.
  • Structure: The granular, template-enforced, and introspectively annotated examples allow for rigorous benchmarking of intermediate model outputs, not just end-task accuracy.
  • Generalization: Models trained on this dataset show improved stability and reliability across both simulation and physical robot deployments.

Current limitations include reliance on synthetic data sources and strict structural constraints in annotation, which might restrict coverage of rare outlier cases or free-form real-world reasoning styles. Future research may extend Nav-CoT-110K by incorporating broader perceptual modalities, adapting CoT annotation strategies to new embodied tasks, and exploring semi-automated or interactive annotation for even finer-grained reasoning traces.

Nav-CoT-110K serves as both a technical benchmark and a methodological foundation for the next generation of interpretable, multi-stage multimodal reasoning systems under partial observability or complex environment constraints.

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