Robustness-Aware Tool Selection and Manipulation Planning with Learned Energy-Informed Guidance
| Type of publication: | Conference paper |
| Citation: | Dong_ICRA_2026 |
| Publication status: | Published |
| Booktitle: | Proc. Intl Conf. on Robotics and Automation (ICRA) |
| Year: | 2026 |
| 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. |
| Main Research Program: | Human-AI Teaming |
| Keywords: | robot manipulation, robotics |
| Projects: |
Idiap |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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