Papers
3
Total Citations
110
H-Index
3
About
Felix Li’s research lies at the critical intersection of reinforcement learning, robotics, and safety, where he tackles two of the field’s most persistent challenges: designing algorithms that can learn from sparse feedback and ensuring reliable behavior under dynamical uncertainty. His most influential contribution is the **SAVED framework** (Safety Augmented Value Estimation from Demonstrations), a model-based RL approach that integrates expert demonstrations to enable safe, sample-efficient policy learning for complex robotic tasks. This work, which has garnered over 90 citations, directly addresses the brittleness of hand-engineered cost functions and the exploration hazards inherent in real-world robotics. Li has also advanced the theory of imitation learning, formalizing the problem of learning from a *converging* supervisor—a scenario where the demonstrator’s policy improves over time, as with a human refining a novel skill. This work, presented at top venues, provides a principled foundation for human-robot collaboration and interactive policy learning. Through his focus on safety-guaranteed, data-efficient algorithms, Felix Li is shaping a future where robots can learn complex behaviors directly from human guidance without compromising on reliability.
Research Focus
Key Achievements
Top Papers
- 1
- 2On-Policy Robot Imitation Learning from a Converging Supervisor12 citations · 2019
- 3