Beyond Isolation: A Unified Benchmark for General-Purpose Navigation

• URL: http://arxiv.org/abs/2605.09441v1
• Date: Sun, 10 May 2026 09:34:05 GMT
• Title: Beyond Isolation: A Unified Benchmark for General-Purpose Navigation
• Abstract summary: We introduce OmniNavBench, a benchmark for cross-skill coordination and cross-embodiment generalization.<n>We present a simulation platform that breaks the reliance on single-morphology evaluation, enabling generalization tests across humanoid, quadrupedal, and wheeled robots.<n>We curate 1779 expert trajectories via human teleoperation, capturing behavioral nuances such as exploratory glance and anticipatory avoidance.
• Score: 25.795669972829018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The pursuit of general-purpose embodied agents is hindered by fragmented evaluation protocols that isolate navigation skills and fixate on specific robot morphologies, failing to reflect real-world scenarios where agents must orchestrate diverse behaviors across varying embodiments. To bridge this gap, we introduce OmniNavBench, a benchmark for cross-skill coordination and cross-embodiment generalization. OmniNavBench introduces three paradigm shifts: (1) Compositional Complexity. We propose composite instructions that interleave sub-tasks from 6 categories (PointNav, VLN, ObjectNav, SocialNav, Human Following and EQA), compelling agents to transition between exploration, interaction, and social compliance within a single episode. (2) Morphological Universality and Sensor Flexibility. We present a simulation platform that breaks the reliance on single-morphology evaluation, enabling generalization tests across humanoid, quadrupedal, and wheeled robots, with a modular sensor interface and 170 environments blending synthetic assets with real-world scans. (3) Demonstrations Quality. Moving beyond shortest-path algorithms, we curate 1779 expert trajectories via human teleoperation, capturing behavioral nuances such as exploratory glance and anticipatory avoidance. Extensive evaluations demonstrate that current methods, despite their claimed unified design, struggle with the complex, interleaved nature of general-purpose navigation. This exposes a critical disparity between existing capabilities and real-world deployment demands, underscoring OmniNavBench as a testbed for the next generation of generalist navigators. Dataset, code, and leaderboard are available at http://omninavbench.cloud-ip.cc.

Related papers

• Inter-X++: A Comprehensive Benchmark for Multimodal Human-Human Interaction Analysis [55.78822230833247]
  We present Inter-X++, a comprehensive and large-scale benchmark designed to empower versatile HHI analysis.<n>Inter-X++ provides 11,388 high-fidelity interaction sequences and over 8.1M frames, featuring precise whole-body movements and detailed finger articulations.<n>We propose OpenHHI, a single and unified HHI representation and modeling framework that jointly optimize interaction reconstruction and semantic understanding.
  arXiv  Detail & Related papers  (2026-08-20T17:51:48Z)
• NavIsaacLab: Generating Realistic Crowd via Parallel Robot Learning for Benchmarking Human-aware Navigation [40.511983590273445]
  This paper presents NavIsaacLab, a comprehensive framework for benchmarking and training human-aware navigation policies.<n>Based on Isaac Lab, the proposed framework employs photo-realistic scene rendering capabilities and supports parallel simulation on GPU.<n>To enhance the realism of human behavior, a data-driven approach is employed that incorporates a trajectory diffusion model and an adversarial motion learning controller.
  arXiv  Detail & Related papers  (2026-06-24T18:08:19Z)
• Qwen-RobotNav Technical Report: A Scalable Navigation Model Designed for an Agentic Navigation System [96.69286664036143]
  We present Qwen-RobotNav, a scalable navigation model built on Qwen-RobotNav.<n>We train Qwen-RobotNav on 15.6M samples; co-training with vision-language data prevents the collapse into reactive action-sequence mappers.<n>Qwen-RobotNav sets new state-of-the-art results across major navigation benchmarks.
  arXiv  Detail & Related papers  (2026-06-16T16:17:44Z)
• VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands [56.029505206531155]
  Vision Guided Agile Interaction Control (VAIC) is a unified framework that bridges the gap between current controllers and humanoid robots.<n>First, a privileged teacher policy masters diverse interaction skills using precise object kinematics and exact environmental states.<n>Second, a deployable student policy distills these capabilities by replacing full body tracking with velocity targets across multiple axes.
  arXiv  Detail & Related papers  (2026-06-08T09:52:55Z)
• Benchmarking Interaction, Beyond Policy: a Reproducible Benchmark for Collaborative Instance Object Navigation [54.6417539730783]
  Question-Asking Navigation (QAsk-Nav) is the first reproducible benchmark for Collaborative Instance Object Navigation (CoIN)<n>We develop Light-CoNav, a lightweight unified model for collaborative navigation that is 3x smaller and 70x faster than existing modular methods.
  arXiv  Detail & Related papers  (2026-03-31T21:45:00Z)
• ManipArena: Comprehensive Real-world Evaluation of Reasoning-Oriented Generalist Robot Manipulation [66.02142169323521]
  Vision-Language-Action models and world models have recently emerged as promising paradigms for general-purpose robotic intelligence.<n>Existing benchmarks are largely simulator-centric, which provide controllability but fail to capture the reality gap caused by perception noise.<n>We introduce ManipArena, a standardized evaluation framework designed to bridge simulation and real-world execution.
  arXiv  Detail & Related papers  (2026-03-30T15:06:41Z)
• Bench-Push: Benchmarking Pushing-based Navigation and Manipulation Tasks for Mobile Robots [6.551328007366227]
  Bench-Push is the first unified benchmark for pushing-based mobile robot navigation and manipulation tasks.<n>It includes a range of simulated environments that capture the fundamental challenges in pushing-based tasks.<n>It also includes novel evaluation metrics to capture efficiency, interaction effort, and partial task completion.
  arXiv  Detail & Related papers  (2025-12-12T17:25:32Z)
• General-Purpose Robotic Navigation via LVLM-Orchestrated Perception, Reasoning, and Acting [5.291702442384798]
  Agentic Robotic Navigation Architecture (ARNA) is a general-purpose framework that equips an LVLM-based agent with a library of perception, reasoning, and navigation tools.<n>At runtime, the agent autonomously defines and executes task-specific navigation tools drawn from modern robotic stacks.<n>ARNA outperforms state-of-the-art EQA-specific approaches.
  arXiv  Detail & Related papers  (2025-06-20T20:06:14Z)
• RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation [15.548637925166986]
  We present a multi-domain framework for training, evaluating and deploying RL-based navigation policies across diverse robotic platforms and operational environments.<n>Our work presents four key contributions: (1) a scalable and modular framework, facilitating seamless robot-task interchangeability and reproducible training pipelines; (2) sim-to-real transfer demonstrated through real-world experiments with multiple robots; and (3) the release of the first open-source API for deploying Isaac Lab-trained policies to real robots.
  arXiv  Detail & Related papers  (2025-05-20T15:48:23Z)
• HA-VLN 2.0: An Open Benchmark and Leaderboard for Human-Aware Navigation in Discrete and Continuous Environments with Dynamic Multi-Human Interactions [64.69468932145234]
  We present HA-VLN 2.0, a unified benchmark introducing explicit social-awareness constraints.<n>Results show that explicit social modeling improves navigation robustness and reduces collisions.
  arXiv  Detail & Related papers  (2025-03-18T13:05:55Z)
• HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments [13.574462966591199]
  We study the problem of robot navigation in dense and interactive crowds with environmental constraints such as corridors and furniture.<n>Previous methods fail to consider all types of interactions among agents and obstacles, leading to unsafe and inefficient robot paths.<n>We propose a structured framework to learn robot navigation policies with reinforcement learning.
  arXiv  Detail & Related papers  (2024-11-19T00:56:35Z)
• CorNav: Autonomous Agent with Self-Corrected Planning for Zero-Shot Vision-and-Language Navigation [73.78984332354636]
  CorNav is a novel zero-shot framework for vision-and-language navigation. It incorporates environmental feedback for refining future plans and adjusting its actions. It consistently outperforms all baselines in a zero-shot multi-task setting.
  arXiv  Detail & Related papers  (2023-06-17T11:44:04Z)
• Navigating to Objects in the Real World [76.1517654037993]
  We present a large-scale empirical study of semantic visual navigation methods comparing methods from classical, modular, and end-to-end learning approaches. We find that modular learning works well in the real world, attaining a 90% success rate. In contrast, end-to-end learning does not, dropping from 77% simulation to 23% real-world success rate due to a large image domain gap between simulation and reality.
  arXiv  Detail & Related papers  (2022-12-02T01:10:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.