Skip to content
Conference Open access

ZONDA: Zero-shot Object Navigation with Dynamic Avoidance in Multi-floor Environments

Jul 2026 · IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications · pp. 57-62 · 0 citations · 24 references
Computer Science

TL;DR

ZONDA, a zero-shot object navigation with dynamic avoidance framework, integrates three core components and can maintain robust navigation on the dynamic benchmark HM3D-DYNA compared to the existing baseline.

Abstract

In Object Goal Navigation task, existing methods are typically restricted to static and single-floor environments, ignoring cross-floor topologies and dynamic pedestrian, which limits their real-world deployment. To address these limitations, we propose ZONDA, a zero-shot object navigation with dynamic avoidance framework. In particular, ZONDA integrates three core components: (i) Heuristic multi-floor planning: from height-difference traversable maps, enables stair traversal and cross-floor exploration without a platform-specific learned controller; (ii) Multi-view target verification: cross-checks multiscale observations with a vision-language model, significantly reducing false positives; and (iii) Dynamic pedestrian avoidance: explicitly tracks and predicts moving pedestrians to generate anticipatory behaviors. Evaluated on a real Direct Drive Tech TITA biped robot and extensive simulations on HM3D and MP3D, ZONDA achieves significantly improved results. Moreover, ZONDA can maintain robust navigation on the dynamic benchmark HM3D-DYNA compared to the existing baseline.

Read PDF

Similar papers

Open access Aug 2026

DevGRU: Depth-Guided Visual Navigation Using a Collision-Aware Recurrent Model

The proposed DevGRU navigation system employs an action predictor that generates collision-aware future trajectories, enabling effective avoidance of immediate obstacles and has a relatively small number of trainable parameters, resulting in the fastest inference time among the baselines.

Kyung Min Han, Eunsom Kim, Young J. Kim · 0 citations
Conference Aug 2026

Predictive Collision Avoidance via Probabilistic Multi-Object Tracking in Dynamic Environments

Collision avoidance for industrial AGVs operating in dynamic, shared environments remains challenging because reactive local planners treat moving obstacles as static, leading to conservative or unsafe behavior. We address this by enabling the local planner to reason over predicted obstacle motion rather than instantan...

Bruk Gebregziabher, Hadush Hailu · 0 citations
Preprint Sep 2026

Multi-Task Visual Perception Network with LLM Conditioning for Autonomous Navigation

This work proposes a user-friendly, interactive framework that eliminates the reliance on globally consistent maps and integrates visual perception with Large Language Models (LLM) to interpret user commands via text or voice.

Praveen Kumar, K. Guruprasad, Tushar Sandhan · 0 citations
Preprint Aug 2026

LifelongCrossNav: Persistent 3D Semantic Memory for Cross-Floor Multi-Object Navigation

Experimental results show that LifelongCrossNav consistently outperforms a representative planar persistent semantic-map baseline on HM3D-MFMON, demonstrating that persistent 3D semantic memory and cross-floor traversability modeling effectively support sequential multi-object navigation in multi-floor environments.

Zehui Li, Zihao Sun, Jia-Wei Xu et al. · 0 citations
Oct 2026

ForexNav: Foresight Exploratory Navigation in Complex and Unknown Indoor Environments

Autonomous navigation in unknown, complex indoor environments remains challenging due to limited sensing range and severe partial observability. Conventional methods rely on local maps without foresight, causing dead-ends and long detours, while local goal selection based on Euclidean distance or frontier coverage fail...

Hong-Yu Song, Yun-Fang Ren, Ji-Gui Miao et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.