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Runyi Zhao

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3 papers

2 author rows

ICML Conference 2025 Conference Paper

DexScale: Automating Data Scaling for Sim2Real Generalizable Robot Control

Guiliang Liu
Yueci Deng
Runyi Zhao
Huayi Zhou 0001
Jian Chen
Jietao Chen
Ruiyan Xu
Yunxin Tai

A critical prerequisite for achieving generalizable robot control is the availability of a large-scale robot training dataset. Due to the expense of collecting realistic robotic data, recent studies explored simulating and recording robot skills in virtual environments. While simulated data can be generated at higher speeds, lower costs, and larger scales, the applicability of such simulated data remains questionable due to the gap between simulated and realistic environments. To advance the Sim2Real generalization, in this study, we present DexScale, a data engine designed to perform automatic skills simulation and scaling for learning deployable robot manipulation policies. Specifically, DexScale ensures the usability of simulated skills by integrating diverse forms of realistic data into the simulated environment, preserving semantic alignment with the target tasks. For each simulated skill in the environment, DexScale facilitates effective Sim2Real data scaling by automating the process of domain randomization and adaptation. Tuned by the scaled dataset, the control policy achieves zero-shot Sim2Real generalization across diverse tasks, multiple robot embodiments, and widely studied policy model architectures, highlighting its importance in advancing Sim2Real embodied intelligence.

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ICLR Conference 2025 Conference Paper

Toward Exploratory Inverse Constraint Inference with Generative Diffusion Verifiers

Runyi Zhao
Sheng Xu
Bo Yue
Guiliang Liu

An important prerequisite for safe control is aligning the policy with the underlying constraints in the environment. In many real-world applications, due to the difficulty of manually specifying these constraints, existing works have proposed recovering constraints from expert demonstrations by solving the Inverse Constraint Learning (ICL) problem. However, ICL is inherently ill-posed, as multiple constraints can equivalently explain the experts' preferences, making the optimal solutions not uniquely identifiable. In this work, instead of focusing solely on a single constraint, we propose the novel approach of Exploratory ICL (ExICL). The goal of ExICL is to recover a diverse set of feasible constraints, thereby providing practitioners the flexibility to select the most appropriate constraint based on the practical needs of deployment. To achieve this goal, we design a generative diffusion verifier that guides the trajectory generation process using the probabilistic representation of an optimal constrained policy. By comparing these decisions with those made by expert agents, we can efficiently verify a candidate constraint. Driven by the verification feedback, ExICL implements an exploratory constraint update mechanism that strategically facilitates diversity within the collection of feasible constraints. Our empirical results demonstrate that ExICL can seamlessly and reliably generalize across different tasks and environments. The code is available at https://github.com/ZhaoRunyi/ExICL.

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NeurIPS Conference 2025 Conference Paper

Uncertainty-aware Preference Alignment for Diffusion Policies

Runqing Miao
Sheng Xu
Runyi Zhao
Wai Kin (Victor) Chan
Guiliang Liu

Recent advancements in diffusion policies have demonstrated promising performance in decision-making tasks. To align these policies with human preferences, a common approach is incorporating Preference-based Reinforcement Learning (PbRL) into policy tuning. However, since preference data is practically collected from populations with different backgrounds, a key challenge lies in handling the inherent uncertainties in people's preferences during policy updates. To address this challenge, we propose the Diff-UAPA algorithm, designed for uncertainty-aware preference alignment in diffusion policies. Specifically, Diff-UAPA introduces a novel iterative preference alignment framework in which the diffusion policy adapts incrementally to preferences from different user groups. To accommodate this online learning paradigm, Diff-UAPA employs a maximum posterior objective, which aligns the diffusion policy with regret-based preferences under the guidance of an informative Beta prior. This approach enables direct optimization of the diffusion policy without specifying any reward functions, while effectively mitigating the influence of inconsistent preferences across different user groups. We conduct extensive experiments across both simulated and real-world robotics tasks, and diverse human preference configurations, demonstrating the robustness and reliability of Diff-UAPA in achieving effective preference alignment. The project page is available at https: //github. com/mr20010112/Diff_UAPA.

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