Orbit-Planner: Towards Latent World Models for On-Orbit Obstacle Avoidance of Satellite Agents

Abstract:Satellite agents for on-orbit navigation tasks need to predict collision risks using limited onboard observations. However, conventional planners often rely on predefined maps and fixed environmental assumptions, limiting their adaptability in dynamic on-orbit scenarios. In this paper, we propose Orbit-Planner, a two-stage latent world model for on-orbit obstacle avoidance. Orbit-Planner learns action-conditioned spacecraft dynamics to perform future-state rollouts in latent space, and introduces a Physics Probe to decode physical state changes from imagined latent trajectories. Experiments demonstrate that Orbit-Planner can perform long-horizon latent rollouts and recover physical states from imagined trajectories. In closed-loop obstacle-avoidance navigation in Isaac Sim, it attains a success rate of 91.7%. Code is available at this https URL.

4 pages, 6 figures. Accepted to AP-GARSS 2026. Project page: this https URL

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2608.16651 [cs.RO]

(or arXiv:2608.16651v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.16651 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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