Physics-Constrained Robot Grasp Planning for Dynamic Tool Use Research Pinned

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.