Fidelity-Aware Data Composition for Robust Robot Generalization
Authors: name: Zizhao Tong, name: Di Chen, name: Sicheng Hu, name: Hongwei Fan, name: Liliang Chen, name: Guanghui Ren, name: Hao Tang, name: Hao Dong, name: Ling Shao

Generalist robot policies trained on large-scale, visually homogeneous
datasets can be susceptible to shortcut learning, which impairs their
out-of-distribution (OOD) generalization. While generative data augmentation is
a common approach to introduce diversity, it presents a subtle challenge: data
composition. Naively mixing real and synthetic data can corrupt the learning
signal, as this process often prioritizes visual diversity at the expense of
information fidelity. This paper suggests that robust generalization depends on
principled, fidelity-aware data composition. We introduce Coherent Information
Fidelity Tuning (CIFT), a framework that treats data composition as an
optimization problem. CIFT uses a practical proxy for Information Fidelity
based on the feature-space geometry of a dataset. This enables the
identification of a phase transition, termed the Decoherence Point, where
training stability degrades. The framework includes a generative engine,
Multi-View Video Augmentation (MVAug), to synthesize a causally disentangled
data spectrum for this tuning process. Applying CIFT to policy architectures
such as pi_0 and Diffusion Policy improves OOD success rates by over 54\%.
These results indicate that fidelity-aware composition, beyond data synthesis
alone, is an important component for developing robust, general-purpose robots.