SAIN: Structure-Aware Interactive Navigation with Active Dialogue Grounding for Mobile Robot

Most existing vision-language navigation tasks assume that instructions are complete and unambiguous, but real-world robots often encounter natural human instructions that are ambiguous, underspecified, or incomplete, requiring them to resolve such uncertainties through active questioning. SAIN presents a zero-shot framework that turns active dialogue into persistent navigation state by compiling oracle answers into target evidence, route-level corridor memory, and object candidate labels. This approach supports long-horizon interactive instance navigation without task-specific policy training.

The gist

SAIN compiles dialogue answers into persistent target evidence, route-level corridor memory, and objectcandidate labels to support a unified policy for frontier ranking and final target approach in interactive instance goal navigation.

How it works

SAIN operates on the dialogue-to-state principle, converting conversations into a persistent internal agent state that maintains value, room, graph, and object memories. This state is structured as:

1. Target Evidence: Updated by information questions to refine target facts used for instance verification and room relevance scoring.

2. Route Guidance: Grounded to topological paths and projected into route and history corridor memories based on route answers.

3. Candidate Labels: Updated by disambiguation questions, enabling the policy to avoid rejected distractors and approach confirmed targets.

During task execution, uncertainty triggers active questioning organized into three types: information, route, and disambiguation questions. Information answers update target evidence; route answers are grounded to topological paths; and disambiguation answers update object-candidate labels. The unified policy then consumes this full state for frontier ranking, exploration, and final target approach.

Structured Maps Representation

SAIN maintains four structured maps that provide long-term memory interfaces:

1. Value map (Vt): Provides a target-conditioned exploration prior over frontiers, scored using a BLIP-2-style matcher against the target prompt. The basic frontier value is defined as sval(ξ) = Mean(Vt(N (ξ))).

2. Graph map (Gt): Skeletonizes explored free space into topological nodes, exposing spatial uncertainty and defining path proposals for route grounding.

3. Room map (Rt): Partitions explored space into roomlike regions using watershed segmentation, storing masks, representative views, frontier counts, and adjacency information. A VLM labels representative views to select the target-relevant room R⋆t for room-level scoring.

4. Object map (Ot): Stores object-centric point clouds and candidate labels. GroundingDINO and MobileSAM produce RGB-D masks which are backprojected into global 3D clouds and associated across frames via KD-tree point-cloud distance to preserve instance consistency over time.

Target Perception and Instance Candidate Reasoning

SAIN employs entropy-aware visual question answering (VQA) to filter unreliable observations from open-vocabulary detectors like GroundingDINO. It then performs similarity-based instance verification in three steps:

1. Entropy-Aware Object Detection: SAIN estimates detection uncertainty from the next-token logits of the VLM rather than a discrete answer, accepting only proposals with low uncertainty (gj = 1).

2. Similarity-Based Instance Verification: For accepted candidates, SAIN uses an LLM to compare candidate evidence with target facts accumulated from information questions to generate verification triples (Qi). It keeps only certain answers and uses them to refine candidate evidence into ˜di.

3. Similarity Scoring: The refined evidence is matched against the target fact set using a normalized LLM-based similarity function, si ∈ [0, 1]. This score maps to a candidate label: REJECTED (si < τlow), PENDING (τlow ≤ si < τhigh), or CONFIRMED (si ≥ τhigh).

Route-Grounded Exploration and Navigation Policy

To ensure route answers persistently guide exploration, SAIN grounds route answers into topological corridors and injects them into frontier scoring.

1. Route-Answer Grounding: When a route answer αt is received, SAIN performs a bounded depth-first search to keep local candidate path proposals around the current node and emits paths using branch-balanced selection based on a multi-objective key ρ(p). The selected path p⋆t is determined by maximizing LMMatch between the route answer and the textual description of candidate paths. This results in a route session Tt = (p⋆t, C⋆t, C¯⋆t, ηt), where C⋆t is the short-term corridor and C¯⋆t is the history corridor.

2. Route Mask and Frontier Scoring: SAIN uses two masks for frontier decisions: Mroute t provides a strong short-term bias around the selected corridor (Mroute t(x) = 1 + βr exp − dist(x, C⋆t)2/2σ2r), while Mhist t preserves a decaying bias along the extended route direction (Mhist t(x) = 1 + Ht(x)).

As a fastidious researcher, I have thoroughly analyzed the SAIN (Structure-Aware Interactive Navigation with Active Dialogue) framework. The core innovation is converting interactive dialogue into persistent, structured navigation states that guide a unified policy for frontier ranking and target approach.

Here are specific improvements derived from the SAIN architecture:

1. Enhance Long-Horizon, Ambiguous Goal Navigation (IIGN)

2. Achieve Zero-Shot Performance without Task-Specific Policy Training

3. Improve Robustness to Information Uncertainty via Structured Evidence Refinement

4. Optimize Exploration Strategy using Multi-Modal, Persistent State Priors

5. Enhance Long-Horizon, Ambiguous Goal Navigation (IIGN)

SAIN's architecture allows the system to transition from ambiguous category-level instructions (Find the chair) to a specific instance search by maintaining persistent memories.

The improved system can:

• Identify and locate a specific object instance among same-category distractors in complex, unseen indoor environments.

• Execute long-horizon navigation tasks where the target is not immediately visible or reachable, relying on accumulated dialogue history (route memory) to guide exploration across multiple rooms and topological branches.

2. Achieve Zero-Shot Performance without Task-Specific Policy Training

The system achieves state conversion without training a task-specific policy, making it highly versatile for new navigation tasks.

The improved system can:

• Deploy in novel indoor environments immediately upon receiving an instruction, requiring zero fine-tuning on the specific environment's dynamics or navigation policies.

• Leverage pre-trained vision and language models (VLM) for perception and reasoning, with dialogue serving as the mechanism to ground these general capabilities into a specific search trajectory.

3. Improve Robustness to Information Uncertainty via Structured Evidence Refinement

The system uses Information Questions to generate target facts, which are then used in similarity-based verification against candidate detections, refined by an LLM operator (evidence refinement).

The improved system can:

• Accurately distinguish between visually similar objects (e.g., two white beds) by comparing visual evidence against specific attributes gathered from the oracle (e.g., placed between two bedside tables).

• Reduce false positives during candidate detection by only accepting detections that are strongly supported by the accumulated target context, effectively filtering unreliable open-vocabulary proposals using entropy-aware VQA and similarity scoring.

4. Optimize Exploration Strategy using Multi-Modal, Persistent State Priors

SAIN integrates four structured maps (Value Map, Graph Map, Room Map, Object Map) and combines them into a unified frontier score:

The improved system can:

• Employ a sophisticated exploration strategy where the selection of the next frontier is not based solely on visual proximity but on a composite score combining target relevance (Value Map), topological connectivity (Graph/Route Mask), room utility (Room Map), and accumulated search history.

• Dynamically switch between exploratory modes: prioritizing Target-Oriented Execution when a candidate is confirmed, or switching to route grounding when exploring an unqueried branch node to ensure the agent follows plausible paths derived from dialogue history.

Most existing vision-language navigation tasks assume that instructions are complete and unambiguous. However, real-world robots often encounter natural human instructions that are ambiguous, underspecified, or incomplete, requiring them to resolve such uncertainties through active questioning. Interactive Instance Goal Navigation (IIGN) requires an embodied agent to find the specific instance under an ambiguous category-level instruction through active dialogue. However, existing dialogue-enabled methods often consume oracle answers as transient textual context for immediate decisions, rather than persistent spatial or object-centric structured state. We present SAIN, a zero-shot framework that turns active dialogue into persistent navigation state. Instead of consuming oracle answers as one-step text hints, SAIN compiles them into target evidence, route-level corridor memory, and object-candidate labels. These states are stored in structured value, room, graph, and object memories, then consumed by a unified policy for frontier ranking and final target approach. On the VL-LN IIGN benchmark, SAIN improves SR from 20.2 to 25.4 and SPL from 13.07 to 14.17 over the strongest reported dialogue-enabled baseline, while requiring no task-specific policy training. The results support dialogue-to-state conversion as an effective zero-shot mechanism for long-horizon interactive instance navigation. Project website: https://zorattc.github.io/SAIN/