PILOT: Guiding Long-Horizon World-Model Planning for Robot Manipulation
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PILOT improves robotic manipulation by combining visual subgoals and policy priors, achieving 68.8% success in real tasks and better planning efficiency over long horizons.
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- arXiv Robotics — research abstractsPILOT: Guiding Long-Horizon World-Model Planning for Robot Manipulation with Visual Subgoals and Policy Priors
The paper presents PILOT, a method that enhances long-horning robotic manipulation by integrating visual subgoals and policy priors. Using a video model to generate intermediate goals and a learned policy to initialize the cross-entropy method (CEM), PILOT achieves 68.8% success in real-robot tasks, outperforming baselines like direct final-goal planning and policy-only execution. Simulation results show consistent gains over longer horizons, with improvements in both task execution and planning efficiency.
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