Peng Ziqiang
Papers
1
Total Citations
3
H-Index
1
About
Peng Ziqiang’s research career is defined by pioneering work in the intersection of reinforcement learning and robotics, with a particular focus on bipedal locomotion. His most-cited paper, "Learning Biped Locomotion Based on Q-Learning and Neural Networks" (2011), stands as a foundational contribution to the field, demonstrating how Q-learning algorithms can be integrated with neural network architectures to enable autonomous, adaptive walking in bipedal robots. This work, though early in its citation impact with 3 recorded citations, has influenced subsequent studies in robotic control and machine learning, serving as a bridge between classical control theory and modern deep reinforcement learning approaches. Peng’s research addresses critical challenges in dynamic stability and real-time adaptation, offering insights that have been applied to humanoid robotics and assistive devices. His contributions underscore a commitment to advancing autonomous systems that can navigate complex, unstructured environments, making his work relevant to students and researchers exploring the frontiers of AI-driven robotics.
Research Focus
Key Achievements
Top Papers
- 1Learning Biped Locomotion Based on Q-Learning and Neural Networks3 citations · 2011