Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation
This paper proposes a novel distributionally robust control method that uses Stein variational inference to model uncertainty in contact-rich manipulation. It combines the strengths of model-based controllers with flexible uncertainty modeling, achieving up to 3× robustness improvement across various tasks.
[2605.19029] Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation
[Submitted on 18 May 2026]
Title:Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation
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Abstract:Reliable robotic manipulation requires control policies that can accurately represent and adapt to uncertainty arising from contact-rich interactions. Modern data-driven methods mitigate uncertainty through large-scale training and computation, and degrade significantly in performance with limited number of training samples. By contrast, classical model-based controllers are computationally efficient and reliable, but their limited ability to represent task-relevant uncertainty can hinder performance in contact-rich interactions.
In this work, we propose to expand the capabilities of model-based manipulation control through more flexible uncertainty modeling that retains performance while exactly adapting to uncertainty. Our approach casts the manipulation problem as a distributionally robust control optimization and proposes a novel deterministic formulation based on Stein variational inference that preserves performance while explicitly modeling task-sensitive parameter uncertainty. As a result, the derived controllers are more aware of task sensitivities to uncertainty, yielding high reliability without compromising performance. Experimental results demonstrate up to 3$\times$ improved robustness across a range of contact-rich manipulation tasks under broad parametric uncertainty, outperforming existing model-based control methods.
Comments: In Proceedings of Robotics: Science and Systems, Sydney, Australia, July 2025
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.19029 [cs.RO]
(or arXiv:2605.19029v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.19029
arXiv-issued DOI via DataCite (pending registration)
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From: Hrishikesh Sathyanarayan [view email] [v1] Mon, 18 May 2026 18:54:29 UTC (38,307 KB)
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