Humans and many animals subconsciously choose robust ways of selecting and using tools, based on years of embodied experience—for example, humans choose a ladle instead of a flat spatula to serve meatballs. However, robustness under uncertainty remains underexplored in robotic tool-use planning. 

This paper presents a robustness-aware framework that jointly selects tools and plans contact-rich manipulation trajectories, explicitly optimizing for robustness against environmental disturbances. At the core of our approach is a learned, energy-based robustness metric, which guides the planner towards robust manipulation behaviors. 

We evaluate our approach across three representative tool-use tasks. Simulation and real-world results demonstrate that our approach consistently selects robust tools and generates disturbance-resilient manipulation plans.