Technical Capabilities
Technical Capabilities
Through CLBF we achieve safety and stability, through transfer learning we ensure generalization and transferability, and through physical-information priors we improve sample efficiency — these three form the theoretical cornerstone for the deployable "cerebellum" of embodied intelligence.
Academic Achievements
01
Optimal Control of Nonlinear Systems Based on Stable and Safe Reinforcement Learning
AIChE Journal (top chemical engineering journal)
02
Transfer Reinforcement Learning Control Algorithm Design and Its Generalization Performance Analysis
IEEE Transactions on Cybernetics
03
Physics-Informed Reinforcement Learning Optimal Control Algorithm
Theoretical framework for embedding physical priors into policy learning
04
Cerebellum Key Algorithm Optimization — Fine Manipulation and Load Mutation
TDSMC (time-delay SMC) sliding mode control