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Exploring reinforcement learning, embodied AI, and robotics simulation through papers, reproducible experiments, and code. I use this space to connect research questions with practical implementations.
| Area | Focus |
|---|---|
| Reinforcement Learning | Policy learning, training workflows, and experimental reproduction |
| Embodied AI | Vision-language-action models, robot manipulation, and navigation |
| Robotics Simulation | Isaac Lab / Isaac Sim, MuJoCo, and locomotion control |
| Research & Engineering | Paper notes, method comparisons, and reusable engineering records |
Public forks I explore for learning and reproduction. Original work belongs to the respective upstream authors.
Explore navigation, simulation, and paper-reading resources
- NavIsaaclab2.0: robot navigation in shared human-robot environments. Upstream: yh83305/NavIsaaclab2.0.
- mujoco_learning: MuJoCo modeling and Python / C++ interfaces. Upstream: Albusgive/mujoco_learning.
- daily-paper-reader: paper recommendations and AI-assisted reading. Upstream: ziwenhahaha/daily-paper-reader.
Read thoughtfully. Reproduce carefully. Build steadily.