Fast Navigation Through Occluded Spaces via Language-Conditioned Map Prediction

• URL: http://arxiv.org/abs/2512.21398v1
• Date: Wed, 24 Dec 2025 19:34:08 GMT
• Title: Fast Navigation Through Occluded Spaces via Language-Conditioned Map Prediction
• Abstract summary: We introduce PaceForecaster as an approach that incorporates such co-pilot instructions into local planners.<n>PaceForecaster takes the robot's local sensor footprint and the provided co-pilot instructions as input.<n>We show that using language-conditioned forecasts and goals improves navigation performance by 36% over a local-map-only baseline while in polygonal environments.
• Score: 18.70552952249317
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In cluttered environments, motion planners often face a trade-off between safety and speed due to uncertainty caused by occlusions and limited sensor range. In this work, we investigate whether co-pilot instructions can help robots plan more decisively while remaining safe. We introduce PaceForecaster, as an approach that incorporates such co-pilot instructions into local planners. PaceForecaster takes the robot's local sensor footprint (Level-1) and the provided co-pilot instructions as input and predicts (i) a forecasted map with all regions visible from Level-1 (Level-2) and (ii) an instruction-conditioned subgoal within Level-2. The subgoal provides the planner with explicit guidance to exploit the forecasted environment in a goal-directed manner. We integrate PaceForecaster with a Log-MPPI controller and demonstrate that using language-conditioned forecasts and goals improves navigation performance by 36% over a local-map-only baseline while in polygonal environments.

Related papers

• EgoNav: Bridging Learned Waypoints and Geometry-Aware Local Control for Robust Indoor Navigation [7.207055231363046]
  Image-goal navigation using lightweight topological maps is a practical paradigm for indoor robot deployment.<n>EgoNav is a hierarchical system that generates candidates from semantically segmented traversable regions.<n>EgoNav consistently outperforms contemporary baselines in both success rate and path efficiency.
  arXiv  Detail & Related papers  (2026-08-26T11:13:07Z)
• LP-NavOA: Integrated Local Navigation and Obstacle Avoidance for Humanoid Robots under Limited Perception [4.290777350661877]
  LP-NavOA is a limited-perception navigation and obstacle-avoidance framework for humanoid robots.<n>At runtime, LP-NavOA uses proprioception, short-range local range sensing, and a body-frame goal direction.<n>In MuJoCo open-wall and indoor layouts, the distilled planner produces obstacle bypassing and post-avoidance goal recovery.
  arXiv  Detail & Related papers  (2026-06-22T12:31:18Z)
• Walk With Me: Long-Horizon Social Navigation for Human-Centric Outdoor Assistance [70.01362005983516]
  Walk with Me is a map-free framework for long-horizon social navigation from high-level human instructions.<n>It combines semantic intent grounding, map-free long-horizon planning, safety-aware reasoning, and low-level action generation.
  arXiv  Detail & Related papers  (2026-04-29T16:02:13Z)
• FloorPlan-VLN: A New Paradigm for Floor Plan Guided Vision-Language Navigation [17.1490555684021]
  Existing-Language Navigation (VLN) task requires agents to follow instructions, ignoring some potentially useful global spatial priors.<n>We introduce bfFloorPlan-VLN, a new paradigm that leverages structured floor plans as global spatial priors to enable navigation with only concise instructions.
  arXiv  Detail & Related papers  (2026-03-18T07:22:48Z)
• Temporally-Sampled Efficiently Adaptive State Lattices for Autonomous Ground Robot Navigation in Partially Observed Environments [2.2188568806866287]
  In traditional autonomous navigation architectures, a regional motion planner will consume the environment map and output a trajectory for the local motion planner to use as a reference.<n>We propose Temporally-Sampled Efficiently Adaptive State Lattices (TSEASL) to consider updated and optimized versions of previously generated trajectories against the currently generated trajectory.<n>Results indicate that when running TSEASL, the robot did not require manual interventions in the same locations where the robot was running the baseline planner.
  arXiv  Detail & Related papers  (2026-02-13T18:14:56Z)
• DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation [73.80968452950854]
  Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural language instructions through free-form 3D spaces.<n>Existing VLN-CE approaches typically use a two-stage waypoint planning framework.<n>We propose DAgger Diffusion Navigation (DifNav) as an end-to-end optimized VLN-CE policy.
  arXiv  Detail & Related papers  (2025-08-13T02:51:43Z)
• 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)
• ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments [56.194988818341976]
  Vision-language navigation is a task that requires an agent to follow instructions to navigate in environments. We propose ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments. ETPNav yields more than 10% and 20% improvements over prior state-of-the-art on R2R-CE and RxR-CE datasets.
  arXiv  Detail & Related papers  (2023-04-06T13:07:17Z)
• Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map [39.54575497596679]
  This work proposes a new representation of traversability based exclusively on robot speed that can be learned from data. The proposed algorithm learns to predict a distribution of speeds the robot could achieve, conditioned on the environment semantics and commanded speed. Numerical simulations demonstrate that the proposed risk-aware planning algorithm leads to faster average time-to-goals.
  arXiv  Detail & Related papers  (2022-03-25T03:08:02Z)
• ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints [94.60414567852536]
  Long-range navigation requires both planning and reasoning about local traversability. We propose a learning-based approach that integrates learning and planning. ViKiNG can leverage its image-based learned controller and goal-directed to navigate to goals up to 3 kilometers away.
  arXiv  Detail & Related papers  (2022-02-23T02:14:23Z)
• Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy [48.51396198176273]
  We propose an integrated system that can perform large-scale autonomous flights and real-time semantic mapping in challenging under-canopy environments. We detect and model tree trunks and ground planes from LiDAR data, which are associated across scans and used to constrain robot poses as well as tree trunk models. A drift-compensation mechanism is designed to minimize the odometry drift using semantic SLAM outputs in real time, while maintaining planner optimality and controller stability.
  arXiv  Detail & Related papers  (2021-09-14T07:24:53Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps [21.245942227850733]
  We propose an approach that leverages a rough 2-D map of the environment to navigate in novel environments without requiring further learning. Because the low-level controller is only trained with local behaviors, this framework allows us to generalize to novel environments. Experimental results demonstrate the effectiveness of the proposed framework in both seen and novel environments.
  arXiv  Detail & Related papers  (2021-06-07T14:42:51Z)

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.