Tool use requires not only selecting appropriate tools but also generating grasps and motions that remain stable under dynamic interactions. Existing approaches largely focus on high-level tool grounding or quasi-static manipulation, overlooking stability in dynamic and cluttered regimes. We introduce iTuP (inverse Tool-use Planning), a framework that outputs robot grasps explicitly tailored for tool use. iTuP integrates a physics-constrained grasp generator with a task-conditional scoring function to produce grasps that remain stable during dynamic tool interactions. These grasps account for manipulation trajectories, torque requirements, and slip prevention, enabling reliable execution of real-world tasks. Experiments across hammering, sweeping, knocking, and reaching tasks demonstrate that iTuP outperforms geometry-based and vision-language model (VLM)-based baselines in grasp stability and task success. Our results underscore that physics-constrained grasping is essential for robust robot tool use in quasi-static, dynamic, and cluttered environments.

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