A new Stein variational controller for nonlinear systems with parametric uncertainty achieves better performance-robustness tradeoffs than worst-case or ensemble baselines by shaping control around task-dependent uncertainty distributions.
Behavior Synthesis via Contact-Aware Fisher Information Maximization
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abstract
Contact dynamics hold immense amounts of information that can improve a robot's ability to characterize and learn about objects in their environment through interactions. However, collecting information-rich contact data is challenging due to its inherent sparsity and non-smooth nature, requiring an active approach to maximize the utility of contacts for learning. In this work, we investigate an optimal experimental design approach to synthesize robot behaviors that produce contact-rich data for learning. Our approach derives a contact-aware Fisher information measure that characterizes information-rich contact behaviors that improve parameter learning. We observe emergent robot behaviors that are able to excite contact interactions that efficiently learns object parameters across a range of parameter learning examples. Last, we demonstrate the utility of contact-awareness for learning parameters through contact-seeking behaviors on several robotic experiments.
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2roles
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Introduces a Stein variational inference-based deterministic formulation for distributionally robust control in contact-rich robotic manipulation, reporting up to 3x improved robustness under parametric uncertainty.
citing papers explorer
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Stein Variational Uncertainty-Adaptive Model Predictive Control
A new Stein variational controller for nonlinear systems with parametric uncertainty achieves better performance-robustness tradeoffs than worst-case or ensemble baselines by shaping control around task-dependent uncertainty distributions.
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Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation
Introduces a Stein variational inference-based deterministic formulation for distributionally robust control in contact-rich robotic manipulation, reporting up to 3x improved robustness under parametric uncertainty.