arXiv subjects

Minwoo Cho

Publications and source records attributed to Minwoo Cho.

4 recordsLinked to original sources

arXiv2026

GROOVE: Geometry-Guided Reduction of Operational-Space Jerk in VLA Execution

Chunked vision language action (VLA) policies execute several commands per query, but jerk within chunks and across replanning boundaries can induce oscillatory motion and sharp actuator transients. We present GROOVE, an online regulator that searches directional correction regions around the raw three dimensional end effector (EEF) path, without retraining or additional VLA inference. It optimizes the new chunk using delivered commands as boundary conditions, reducing boundary and within chunk jerk while bounding cumulative translation and local axis angle deviation from the raw plan after every command. Using quadratic programs (QPs), GROOVE generates a cube reference and thirteen directional candidates, then selects the one with the lowest command space jerk under a reference relative deviation cap. On a held out LIBERO benchmark, GROOVE achieves the largest reductions among the evaluated methods, reducing translational and rotational EEF jerk by 33.02% and 43.42%, respectively, with task success of 95.75% versus 93.75% for raw execution. Across 50 matched UR5e pairs with measured execution timing, it reduces translational and rotational tool center point (TCP) jerk by 16.39% and 19.49% and joint current slew by 29.09%.

cs.RO↗

arXiv2026

Loss-Optimized Reconfigurable Nonlocal Metasurface-aided Cavity Antenna

This paper presents the design and experimental demonstration of a reconfigurable cavity excited nonlocal metasurface antenna capable of wide angle dynamic beam steering. The antenna is synthesized using a volume surface integral equation based framework that rigorously captures nonlocal mutual coupling among metasurface unit cells. To ensure physical consistency, the numerically characterized resistance and reactance relationship of the tunable unit cells is directly incorporated into the synthesis, enabling precise far-field synthesis while minimizing Ohmic losses. The proposed approach is applied to a 10 GHz cavity fed metasurface antenna composed of 24 independently controlled varactor-loaded unit cells. Numerical simulations and near-field measurements demonstrate stable beam steering with a range of 80 degrees across broadside with excellent agreement between measured and simulated radiation patterns. These results confirm the effectiveness of the proposed framework for the realization of compact, reconfigurable cavity-excited metasurface antennas.

physics.app-ph↗

arXiv2026

Interactive Distillation for Cooperative Multi-Agent Reinforcement Learning

Knowledge distillation (KD) has the potential to accelerate MARL by employing a centralized teacher for decentralized students but faces key bottlenecks. Specifically, there are (1) challenges in synthesizing high-performing teaching policies in complex domains, (2) difficulties when teachers must reason in out-of-distribution (OOD) states, and (3) mismatches between the decentralized students' and the centralized teacher's observation spaces. To address these limitations, we propose HINT (Hierarchical INteractive Teacher-based transfer), a novel KD framework for MARL in a centralized training, decentralized execution setup. By leveraging hierarchical RL, HINT provides a scalable, high-performing teacher. Our key innovation, pseudo off-policy RL, enables the teacher policy to be updated using both teacher and student experience, thereby improving OOD adaptation. HINT also applies performance-based filtering to retain only outcome-relevant guidance, reducing observation mismatches. We evaluate HINT on challenging cooperative domains (e.g., FireCommander for resource allocation, MARINE for tactical combat). Across these benchmarks, HINT outperforms baselines, achieving improvements of 60% to 165% in success rate.

cs.LG↗

arXiv2025

ILCL: Inverse Logic-Constraint Learning from Temporally Constrained Demonstrations

We aim to solve the problem of temporal-constraint learning from demonstrations to reproduce demonstration-like logic-constrained behaviors. Learning logic constraints is challenging due to the combinatorially large space of possible specifications and the ill-posed nature of non-Markovian constraints. To figure it out, we introduce a novel temporal-constraint learning method, which we call inverse logic-constraint learning (ILCL). Our method frames ICL as a two-player zero-sum game between 1) a genetic algorithm-based temporal-logic mining (GA-TL-Mining) and 2) logic-constrained reinforcement learning (Logic-CRL). GA-TL-Mining efficiently constructs syntax trees for parameterized truncated linear temporal logic (TLTL) without predefined templates. Subsequently, Logic-CRL finds a policy that maximizes task rewards under the constructed TLTL constraints via a novel constraint redistribution scheme. Our evaluations show ILCL outperforms state-of-the-art baselines in learning and transferring TL constraints on four temporally constrained tasks. We also demonstrate successful transfer to real-world peg-in-shallow-hole tasks.

cs.RO↗