Haohong Lin
Papers
2
Total Citations
11
H-Index
2
About
Haohong Lin is a rising researcher at the intersection of robotics, reinforcement learning, and safe AI, whose work tackles two of the field’s most pressing challenges: enabling robots to perform complex, long-horizon manipulation tasks and ensuring that learning algorithms operate safely. Lin’s most notable contribution is the **Tactile Ensemble Skill Transfer (TEST)** framework, a pioneering offline reinforcement learning approach that leverages tactile feedback to generalize robotic skills for furniture assembly—a notoriously difficult problem due to its non-repetitive, multi-step nature. This work, published in 2024, has already garnered 6 citations, signaling its immediate impact on the robotics community. Complementing this, Lin co-developed a comprehensive **benchmarking suite for offline safe reinforcement learning** (2023, 5 citations), providing standardized datasets and evaluation protocols that are essential for advancing safety-critical learning algorithms. This dual focus—on both capability and safety—positions Lin as a key contributor to the next generation of autonomous systems. With a clear trajectory toward bridging tactile sensing and reinforcement learning, Haohong Lin’s work is shaping how robots learn to interact with the physical world reliably and safely.
Research Focus
Key Achievements
Top Papers
- 1
- 2Datasets and Benchmarks for Offline Safe Reinforcement Learning5 citations · 2023