arXiv:2602. 20055v2 Announce Type: replace-cross Abstract: Robot navigation typically assumes an obstacle-free path exists between start and goal.
arXiv:2606. 30696v1 Announce Type: cross Abstract: Enabling robots to follow natural language commands to complete zero-shot long-horizon tasks remains challenging.
arXiv:2606. 31919v1 Announce Type: cross Abstract: Zero-shot Object Goal Navigation (ZSON) with RGB-only perception poses a fundamental challenge for embodied agents, as the absence of explicit depth information introduces severe physical uncertainty and semantic-physical misalignment.
arXiv:2603. 08862v2 Announce Type: replace-cross Abstract: Autonomous navigation in highly constrained environments remains challenging for mobile robots.
arXiv:2503. 22122v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have demonstrated remarkable capabilities in robotic planning, particularly for long-horizon tasks that require a holistic understanding of the environment for task decomposition.
arXiv:2607. 08024v1 Announce Type: cross Abstract: Long-horizon robot planning requires jointly reasoning over semantic task structure and geometric feasibility.
arXiv:2510. 00182v2 Announce Type: replace-cross Abstract: While we know that large language models (LLMs) can solve some planning problems, we do not understand the extent of these capabilities for robotics.
Conventional visual navigation policies often struggle with myopic decision-making and mode collapse in complex environments. While world models offer a promising alternative, existing paradigms typically isolate perception, generation, and control, failing to capture their shared spatio-temporal dynamics.
Navi-Agent is a zero‑shot Vision‑Language Navigation in Continuous Environments (VLN‑CE) agent that builds a coordinate‑free spatial state from visual observations and motion history. It represents this state as a navigation topology with nodes as visual places and edges as motion transitions, enabling observation‑based self‑localization, task progress verification, and recovery. Experiments on a zero‑shot VLN‑CE benchmark and real‑world robot platforms demonstrate that Navi‑Agent achieves state‑of‑the‑art performance among geometry‑constrained methods while remaining competitive with geometry‑based approaches.
arXiv:2607. 15641v1 Announce Type: cross Abstract: Humans combine reasoning and motor control to solve complex manipulation tasks under diverse constraints.
CueNav is a video model-based navigation framework that uses visual cues—a Bird's-Eye View map for global task context and a body-aware egocentric view for embodiment context—to guide a video planner. The framework couples this planner with an embodiment-specific Inverse-Dynamics Model that translates dense flow fields from the video plan into robot actions. Experiments show that CueNav nearly doubles maze navigation success compared to cue-less planning and achieves 70% success in narrow passages, while also supporting zero-shot semantic-conditioned navigation across different robot platforms.
arXiv:2608.23354v1 Announce Type: cross Abstract: Autonomous indoor navigation requires both semantic understanding and precise geometric control. We propose OptiSight, a hybrid framework that combin...
arXiv:2609.06476v1 Announce Type: cross Abstract: Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires an embodied agent to navigate unseen environments by following natural la...