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About this paper
Luís Marques, Kristian Popov, Dmitry Berenson
Calibrated Particle-sets for Trans-dimensional Uncertainty Representation (CaPTURe), developed at the University of Michigan, provides probabilistically valid, contact-aware prediction regions for robotic systems. This method yields up to 30% absolute improvement in task success rates and higher efficiency in contact-rich tasks like peg insertion by adapting uncertainty estimates to distinct contact modes.
Key insights
- Uncertainty in contact-rich robotic tasks is highly heterogeneous and trans-dimensional, requiring adaptive, geometry-aware calibration rather than global or parametrically limited approaches.
- Conditioning uncertainty estimates on the predicted future contact stratum allows for probabilistically valid and tightly bounded prediction regions that respect the physical constraints of the environment.
- Using particle-based representations with conformal prediction effectively quantifies complex, non-parametric distributions of future states that arise from physical interactions and model inaccuracies.
Summary by alphaXiv.
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