3 papers
cs.RO2026
A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach
Jia Hu, Yang Chang, Haoran Wang
Motion planning for autonomous driving (AD) faces a critical trade-off. While traditional rule-based pipelines offer verifiable safety and interpretability, they often fail to gene…
cs.LG2025
DDOT: A Derivative-directed Dual-decoder Ordinary Differential Equation Transformer for Dynamic System Modeling
Yang Chang, Kuang-Da Wang, Ping-Chun Hsieh +2
Uncovering the underlying ordinary differential equations (ODEs) that govern dynamic systems is crucial for advancing our understanding of complex phenomena. Traditional symbolic r…
cs.LG2025
When Maximum Entropy Misleads Policy Optimization
Ruipeng Zhang, Ya-Chien Chang, Sicun Gao
The Maximum Entropy Reinforcement Learning (MaxEnt RL) framework is a leading approach for achieving efficient learning and robust performance across many RL tasks. However, MaxEnt…