Papers
7
Total Citations
217
H-Index
5
About
Chen Tang is an emerging researcher at the intersection of deep reinforcement learning, robotics, and autonomous systems. His work spans several high-impact areas, including deep RL for real-world robotic applications, hierarchical planning, explainable autonomous driving, and imitation learning — fields that are rapidly reshaping how intelligent machines perceive, reason, and act. Tang's most influential contribution is his comprehensive survey on deep reinforcement learning for robotics, which has accumulated over 150 citations across multiple versions, reflecting its broad adoption as a foundational reference in the field. This work systematically maps the landscape of real-world RL successes, bridging the gap between theoretical advances and practical deployment in robotic systems. His 2022 paper on hierarchical planning through goal-conditioned offline RL (37 citations) addresses a critical challenge in safe, temporally extended robotic tasks, proposing novel approaches where exploration is costly or dangerous. Tang has also advanced explainable AI in autonomous driving through grounded relational inference frameworks, and explored imitation learning for high-performance autonomous racing via his BeTAIL system. Collectively, his research emphasizes making autonomous systems not only capable but also interpretable and safely deployable — qualities essential for real-world human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes99 citations · 2024
- 2Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes58 citations · 2025
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