Foresight: Iterative Reasoning About Clues that Matter for Navigation
Foresight is a test-time framework that uses a finetuned Vision-Language Model to iteratively propose and critique motion plans for mapless navigation from sparse language instructions. It learns a reward model from human feedback and post-trains the VLM with reinforcement learning, achieving 37% higher task success and 52% fewer interventions in real-world environments.
[2606.12550] Foresight: Iterative Reasoning About Clues that Matter for Navigation
[Submitted on 10 Jun 2026]
Title:Foresight: Iterative Reasoning About Clues that Matter for Navigation
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Abstract:Open-world mapless navigation from sparse language instructions requires resolving underspecified goals and inferring which environmental cues are relevant for reaching the goal. For instance, reaching an out-of-view destination may require interpreting ramps, signs, or detours that reveal where to go or which route to take. Prior works are limited by their reliance on known navigation factors and closed-set factor categories, or identify cues before motion planning and miss plan-dependent cues. We argue that pretrained Vision-Language Models (VLMs) can discover novel instruction-relevant cues, but require adaptation to focus on which cues matter and how they should influence motion planning. We realize these ideas in Foresight, a test-time framework in which a finetuned VLM alternates between proposing image-space motion plans and critiquing them using the language goal and visual context. Subsequent plans are conditioned on prior critiques, enabling iterative motion refinement before execution. To align plan critiques and refinements with open-set behavior preferences, we learn a reward model from human feedback and use it to post-train the VLM with reinforcement learning in the plan-critique loop. In offline evaluations and 6 real-world environments, Foresight improves average task success by 37% and reduces interventions per mission by 52% relative to state-of-the-art test-time reasoning and foundation-model baselines, while running in real-time on a Jetson AGX Orin. We will release code, data, and training details to support future work on test-time reasoning for robot motion refinement. Additional videos at: this https URL
Comments: 22 pages, 10 figures, 3 tables
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.12550 [cs.RO]
(or arXiv:2606.12550v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.12550
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Arthur Zhang [view email] [v1] Wed, 10 Jun 2026 18:01:06 UTC (4,466 KB)
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