On Surprising Effects of Risk-Aware Domain Randomization for Contact-Rich Sampling-based Predictive Control

IEEE ICRA 2026 Workshop CR2 Submission12 Authors

Keywords: mpc, sampling based optimization, contact rich control, domain randomization

TL;DR: We study risk-aware domain randomization in contact-rich predictive sampling and find that, beyond improving robustness to model error, it can fundamentally reshape the optimization landscape by expanding or shrinking the basin of attraction.

Abstract: Domain randomization (DR) is widely used in policy learning to improve robustness to modeling error, but remains underexplored in contact-rich sampling-based predictive control (SPC), where rollout quality is highly sensitive to uncertainty. In this work, we take the first step by studying risk-aware DR in predictive sampling on a simple yet representative Push-T task, comparing average, optimistic, and pessimistic rollout aggregations under randomized model instances. Our initial results suggest that DR affects not only robustness to model error, but also the effective cost landscape seen by the sampling-based optimizer, by reshaping the basin of attraction around contact-producing actions. This opens up potential for exploring better grounded risk-aware contact-rich SPC under model uncertainty. Video: https://youtu.be/f1F0ALXxhSM

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Submission Number: 12