Learning energy-efficient humanoid locomotion requires discovering mechanically economical gait coordination, not merely reducing actuator effort. Reinforcement learning promotes efficiency through effort-related reward penalties, which guide the step-to-step mechanics of walking only indirectly. This article proposes...
Hyeonjin Choi, Joongheon Kim, Daekyum Kim· 0 citations
Two-tiered organization provides a compact survey lens for autonomous control (AC): an upper tier supports learning, planning, and high-level (HL) mission reasoning, while a lower tier executes feedback control, safety filtering, and platform-specific constraints. This compact survey reviews hierarchical architectures...
Junseo Min, Hoyeong Lee, Hyojun Ahn et al.· International Conference on...· 0 citations
Robots with kinematic loops, coupled actuators, and changing contacts require consistent models of configuration, motion, force, and dynamics. Yet these interfaces are often reconstructed separately for control and simulation, making closure and actuation consistency difficult to maintain. This paper presents RoboCompi...
Mehdi Heydari Shahna, Joongheon Kim, J. Mattila· 0 citations
This paper proposes an offline reinforcement learning framework that combines a transformer architecture with conservative Q-learning (CQL) for torpedo evasion of autonomous underwater vehicles (AUVs). Conventional approaches that feed only a single-step observation into a multilayer perceptron (MLP) struggle to captur...
Hyojun Ahn, Hoyeong Lee, Boseon Kang et al.· IEEE Access· 0 citations
This paper proposes SafeGPT, a hierarchical framework that integrates generative pretrained transformer (GPT)-based large language models (LLM) with safe reinforcement learning (safe-RL). SafeGPT addresses the large-scale random multi-point tour problem (RMPT) for multiple unmanned aerial vehicles (UAVs). The target pl...
Hyojun Ahn, Seungcheol Oh, Gyusun Kim et al.· IEEE Transactions on Network...· 0 citations
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