Abstract

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.

Bibtex reference

@article{Dong26ICRA,
	author={Dong, Y. and Zhang, Y. and Calinon, S. and Pokorny, F. T.},
	title={Robustness-Aware Tool Selection and Manipulation Planning with Learned Energy-Informed Guidance},
	booktitle={Proc.\ {IEEE} Intl Conf.\ on Robotics and Automation ({ICRA})},
	year={2026},
	pages={}
}

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