CONF Dong_ICRA_2026/IDIAP Robustness-Aware Tool Selection and Manipulation Planning with Learned Energy-Informed Guidance Dong, Y. Zhang, Yan Calinon, Sylvain Pokorny, F. T. robot manipulation robotics EXTERNAL http://publications.idiap.ch/attachments/papers/2026/Dong_ICRA_2026.pdf PUBLIC Proc. Intl Conf. on Robotics and Automation (ICRA) 2026 Humans subconsciously choose robust ways of selecting and using tools, based on years of embodied experience—for example, choosing 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 formulate a hierarchical optimization pipeline that first identifies a tool and configuration that optimizes robustness, and then plans a corresponding manipulation trajectory that maintains robustness throughout execution. 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.