GODHS: Hierarchical Heuristic Robotic Search

• GODHS is a hierarchical object search paradigm that fuses LLM-driven semantic reasoning with multi-level decision making to reduce the search space.
• It integrates semantic perception and heuristic-guided motion planning to prioritize candidate areas—rooms, carriers, and features—for efficient target location.
• Empirical evaluations show GODHS achieves lower redundant search rates and improved efficiency compared to traditional static mapping and random exploration.

Goal-Oriented Dynamically Heuristic-Guided Hierarchical Search (GODHS) is an architectural paradigm for robot and agent object search that integrates LLMs, semantic perception, structured multi-level reasoning, and heuristic motion planning to efficiently locate targets within complex, unfamiliar environments. By fusing symbolic (language) and geometric (spatial) inference within a strict decision hierarchy, GODHS overcomes limitations of static mapping and undirected exploration, enabling efficient, context-aware search that is robust to environmental complexity and uncertainty (Zhang et al., 28 Aug 2025).

1. Hierarchically Structured Decision Framework

GODHS adopts a strict, five-level search hierarchy designed to mirror human intuitions for object finding:

Level	Semantic Scope	Example Reasoning
Scene	Global environment context	Flat composed of several rooms
Room	Local regions or functional spaces	Kitchen, bedroom, living room
Carrier	Objects likely to host the item	Fridge, table, shelf
Feature	Specific parts of carriers	"inside" fridge, "on" table
Item	Target object (endpoint of the search)	Orange, keys, cup

At each level, the system uses semantic input and LLM-driven inference to iteratively reduce the search space. After initially building a global map (using LiDAR and RGB semantic segmentation), the system infers room categories (e.g., kitchen, bedroom) and ranks their relevance for the target object. Detected objects are classified as potential "carriers" (e.g., a fridge for an orange, a bed for a pillow) via LLM-based reasoning with prompt templates and example-driven candidate sets. For each carrier, the system further identifies and ranks sub-regions—such as "inside," "top," or "sides"—to physically search, thus generating a set of prioritized candidate feature regions for the item search phase.

This hierarchical decomposition prunes the combinatorial search space and enables the system to emulate human strategies: first find the right room, then the likely object, then the exact location on or inside the object.

2. LLM Integration and Semantic Guidance

LLMs serve as the semantic reasoning engine throughout GODHS:

• Room inference: By analyzing detected objects (e.g., bed, dressing table), the LLM infers room types ("bedroom").
• Carrier/feature ranking: Structured prompts are used to query the LLM for a probability-ordered list of carriers and features relevant to the target (e.g., for an "orange," fridge > table > shelf).
• Structured prompt design: Multi-stage validation enforces logical constraints, canonical response formats, and feedback-driven correction to ensure outputs remain interpretable and robust for robotic control (see Fig. 3 (Zhang et al., 28 Aug 2025)).
• Hallucination mitigation: Cleaned semantic data and explicit task boundaries are used to neutralize spurious LLM outputs, maintaining reliability in reasoning and translation to physical search actions.

LLMs transfer common-sense, world knowledge (e.g., "pillow likely on bed" or "orange likely inside fridge") into machine-executable directives at each search level. This semantic-awareness drastically reduces time wasted on implausible search branches and allows zero-shot adaptation in environments lacking annotated priors.

3. Heuristic-Guided Motion Planning and Coverage Strategies

To physically realize the carrier and feature-level search, GODHS instantiates a geometry-aware, heuristic motion planner that generates both chassis (CH) and end-effector (EE) poses for efficient manipulation and perception coverage (Zhang et al., 28 Aug 2025).

• Surface extraction: Point cloud data for carriers is segmented into top, side, bottom, and inside features; e.g., the top surface is selected by: ℳ_F^top = { (x_F, y_F, z_F) | z_F = max{ z' | (x_F, y_F, z') ∈ ℳ_C } }.
• Visual coverage: A greedy set cover ensures selected EE poses together observe all required feature points (camera cone constraint).
• Inverse kinematics: Candidate CH–EE pose pairs are validated for collision-freeness and kinematic reachability using the Levenberg–Marquardt algorithm.
• Pose sorting: EE poses are sorted lexicographically (by z, y, x, roll, pitch, yaw) for orderly exploration within a feature. CH poses are sorted by polar angle relative to carrier centroid:

$$\rho = \arctan_2(y_{CH} - \bar{y}, x_{CH} - \bar{x})$$

yielding a smooth, clockwise navigation trajectory around the carrier and minimizing redundant motion.

Combining semantic ranking and heuristic trajectory optimization enables rapid and non-redundant path planning to physically check the prioritized features of selected carriers.

4. Reliability through Structured Reasoning and Logical Constraints

Achieving robust reasoning with LLMs in real-world robotics necessitates explicit enforcement of logical, machine-verifiable constraints at each level:

• Clean room and carrier lists are constructed using explicit object labels filtered by semantic segmentation.
• All LLM prompts are augmented with clearly specified expected output formats, clarifying examples, and rejection criteria for non-conforming outputs.
• Multi-turn prompt chaining and feedback correction (see Fig. 3) are used to catch inconsistencies and reinforce valid response types.
• Predefined feature candidate sets ({top, bottom, sides, inside}) are enforced to mitigate semantic drift.

This rigorous prompt and response pipeline constrains LLM proposals to feasible, interpretable actions compatible with downstream robotic modules.

5. Empirical Evaluation and Performance Metrics

Simulation testing was performed in NVIDIA Isaac Sim, using canonical flat environments containing multiple functional zones. Tasks such as finding an "orange" inside a fridge required multi-stage reasoning: identifying rooms, prioritizing carriers, and inspecting carrier features according to semantic ranking.

Performance is measured by search rate metrics:

• $R_r$ (Room search rate): Percent of rooms checked before finding the target.
• $R_c$ (Carrier search rate): Percent of plausible carriers checked.
• $R_i$ (Item search rate): Percent of candidate item features checked.

The overall search rate (OSR) is a weighted sum:

$\mathrm{OSR} = w_1 R_r + w_2 R_c + w_3 R_i$

with typical weights $w_1 = 0.2$, $w_2 = 0.3$, $w_3 = 0.5$.

Results show that GODHS with structured LLM reasoning (GPT-4o) achieves OSR ≈ 21.03%, outperforming both full coverage (≈ 60.51%) and random walk baselines. Heuristic pose sorting further reduces redundant path length and execution time (Zhang et al., 28 Aug 2025).

6. Applications and Scalability

The GODHS paradigm directly supports applications in:

• Household robotics: Rapidly locating lost or misplaced objects in complex home settings, exploiting both language-based inference and physical affordances.
• Industrial automation: Efficient warehouse or factory item retrieval, especially where item placement rules follow tacit knowledge (semantic priors) and environments are only partially mapped.
• Generalized mobile manipulation: Handling dynamic, cluttered, and previously unseen spaces where static object maps or rules are insufficient.

GODHS's semantic "planning-as-programming" enables scalability to new environments and domains without extensive re-training or pre-programmed search routines, provided LLMs have sufficient commonsense grounding.

7. Future Directions and Research Challenges

Possible directions for extension include:

• Enabling continual learning by updating LLM prompt templates and semantic ranking rules with experience-based feedback.
• Integration with advanced multi-modal perception (e.g., 3D scene graphs) to refine carrier and feature extraction.
• Real-world deployment and adaptation: Moving from high-fidelity simulation to real, dynamic environments to test robustness of the semantic spatial hierarchy.
• Extension to collaborative/multi-agent search settings with dynamic environment sharing.

Ensuring reliable, real-time LLM reasoning and scaling prompt validation in open-world deployments are ongoing challenges for GODHS-inspired architectures.

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In summary, Goal-Oriented Dynamically Heuristic-Guided Hierarchical Search combines structured semantic reasoning (via LLMs and hierarchical decomposition) and geometry-aware heuristic planning to realize efficient, interpretable, and robust object search in complex settings (Zhang et al., 28 Aug 2025). The paradigm unifies commonsense symbolic inference and low-level reactive motion with a logically grounded, multi-level control architecture.