Author

Matthew Kim

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Preprint Sep 2026

FINGR: Learning Dexterous Hand Control for Real-World Rubik's Cube Solving

Manipulating a Rubik's Cube with a single dexterous hand is a challenging test of sustained, contact-rich control: the hand must execute successive layer turns while keeping the cube secure. Each turn requires some fingers to support the cube while others push a moving layer, release contact, and reset for the next mov...

Yutong Liang, Quanquan Peng, Matthew Kim et al. · 0 citations

#machine learning Preprint Sep 2026

Safe Score Matching: Diffusion Policies with Hamilton-Jacobi Reachability for Online Safe Reinforcement Learning

Online safe reinforcement learning (RL) seeks policies that maximize reward while satisfying safety constraints. A popular line of research in safe RL relaxes safety to a soft expected-cost constraint and solves the resulting Constrained Markov Decision Process via primal-dual Lagrangian updates that only enforce safet...

Bo-Yang Li, Matthew Kim, Sylvia L. Herbert · 0 citations

#machine learning Preprint Sep 2026

Constrained Flow Policy Updates: A Generalized Schr\"odinger Bridge View

This work builds on the density-free kinetic-energy regularizer of FLAC, a recent reward-only method, and proposes Reparameterized Augmented-Lagrangian Flow Actor with Least Energy (RAFALE), an off-policy actor-critic method for safe RL.

Bo-Yan Li, Matthew Kim, Sylvia L. Herbert · 0 citations