Byeongguk Jeon

JaeHyeok Doo, Byeongguk Jeon, Seonghyeon Ye et al.

There is growing interest in utilizing flow-based models as decision-making policies in reinforcement learning due to their high expressive capacity. However, effectively leveraging this expressivity for value maximization remains challenging, as naive gradient-based optimization requires backpropagating through numerical solvers and often leads to instability. Existing approaches typically address this issue by restricting the expressive capacity of flow-based policies, resulting in a trade-off between optimization stability and representational flexibility. To resolve this, we introduce Q-Flow, a framework that leverages the deterministic nature of flow dynamics to explicitly propagate terminal trajectory value to intermediate latent states along the policy-induced flow. This formulation enables stable policy optimization using intermediate value gradients without unrolling the numerical solver, effectively bridging the gap between stability and expressivity. We evaluate Q-Flow in the offline learning setting on the challenging OGBench suite, where it consistently outperforms state-of-the-art baselines by an average of 10.6 percentage points, while also enabling stable online adaptation within the same framework.

Haeone Lee, Byeongguk Jeon, Suchae Jeong et al.

In robot learning, scaling training datasets across diverse embodiments and environments has become a dominant paradigm for learning generalizable robot policies. These policies are commonly trained via behavior cloning to imitate actions from pre-collected demonstrations. However, since robot actions are tied to the dynamics of the data collection robot, different robots may require different actions to achieve the same motion. This discrepancy hinders both policy training and deployment across diverse robots. To address this, we propose using Cartesian state delta as a universal action representation across robots, and introduce State Prediction and Adaptive Command Execution (SPACE) framework. SPACE handles robot dynamics variation at three levels: across different embodiments, across hardware units of the same embodiment, and within a single robot during operation. It consists of two components: (i) a Cartesian state delta policy that predicts geometric end-effector displacement, and (ii) Action Adapter, which converts the predicted Cartesian state delta into robot-specific control commands. Experiments show that SPACE substantially outperforms policies that directly predict control commands when learning from data collected across different embodiments and across hardware units of the same embodiment. SPACE also remains robust under dynamics shifts at deployment, including changes in control frequency, object weight, and controller gains. The project page is available at http://haeone.site/space-website/.