STRIVE: Structured Representation Integrating VLM Reasoning for Efficient Object Navigation (2505.06729v1)

Abstract: Vision-LLMs (VLMs) have been increasingly integrated into object navigation tasks for their rich prior knowledge and strong reasoning abilities. However, applying VLMs to navigation poses two key challenges: effectively representing complex environment information and determining \textit{when and how} to query VLMs. Insufficient environment understanding and over-reliance on VLMs (e.g. querying at every step) can lead to unnecessary backtracking and reduced navigation efficiency, especially in continuous environments. To address these challenges, we propose a novel framework that constructs a multi-layer representation of the environment during navigation. This representation consists of viewpoint, object nodes, and room nodes. Viewpoints and object nodes facilitate intra-room exploration and accurate target localization, while room nodes support efficient inter-room planning. Building on this representation, we propose a novel two-stage navigation policy, integrating high-level planning guided by VLM reasoning with low-level VLM-assisted exploration to efficiently locate a goal object. We evaluated our approach on three simulated benchmarks (HM3D, RoboTHOR, and MP3D), and achieved state-of-the-art performance on both the success rate ($\mathord{\uparrow}\, 7.1\%$) and navigation efficiency ($\mathord{\uparrow}\, 12.5\%$). We further validate our method on a real robot platform, demonstrating strong robustness across 15 object navigation tasks in 10 different indoor environments. Project page is available at https://zwandering.github.io/STRIVE.github.io/ .

STRIVE: Structured Representation Integrating VLM Reasoning for Efficient Object Navigation

The paper "STRIVE: Structured Representation Integrating VLM Reasoning for Efficient Object Navigation" presents a novel approach to enhancing object navigation tasks in robotics through the integration of Vision-LLMs (VLMs). The framework, STRIVE, addresses two main challenges in applying VLMs to object navigation: effective representation of complex environmental information and strategic querying of VLMs.

Key Features of the STRIVE Framework

STRIVE introduces a multi-layer environmental representation that incrementally builds a structured understanding as the agent navigates. This representation consists of:

• Viewpoint Nodes: These nodes are selected based on a coverage range and represent key locations within the environment, facilitating efficient intra-room exploration.
• Object Nodes: These nodes incorporate open-vocabulary detection and segmentation to provide semantic information about observed objects, assisting in target localization.
• Room Nodes: Defined by environmental segmentation, these nodes enable room-level reasoning and efficient inter-room planning.

Based on this representation, STRIVE employs a two-stage navigation policy that integrates high-level VLM-guided room planning with low-level VLM-assisted intra-room exploration. This structured approach effectively mitigates the over-reliance on VLMs for navigation decisions at each step, thus enhancing navigation efficiency by reducing redundant actions.

Experimental Validation and Results

The framework was evaluated on simulated benchmarks such as HM3D, RoboTHOR, and MP3D, demonstrating state-of-the-art performance with an increase in success rate by 7.1% and path efficiency by 12.5%. Furthermore, real-world tests on an autonomous platform showed robust performance across varied indoor environments, highlighting STRIVE’s practical applicability and resilience to real-world challenges such as sparse point clouds and cluttered spaces.

Implications and Future Directions

The implications of STRIVE are multifaceted:

• Theoretical Implications: The approach provides a structured method for integrating VLMs into robotics by leveraging multiple levels of environmental abstraction, which could enhance understanding of space and semantics in other AI-driven applications.
• Practical Implications: The framework's ability to improve navigation efficiency and object localization in real-time environments indicates its potential utility in a range of robotic applications, from domestic robots to autonomous vehicles.

However, the work also acknowledges limitations, such as the dependency on dense and accurate depth input in simulations, and challenges in real-world deployments due to sparse data acquisition. Future work could explore integrating point cloud completion techniques to address these issues, improving the system's robustness and speed.

In summary, STRIVE presents a sophisticated methodology for enhancing robot navigation through structured environmental understanding and strategic use of Vision-LLMs, marking a significant advancement in the field of embodied AI.