Student Training

Goal for PhD

Problem solving
- Quickly grasp a new problem domain
- Systematically analyze problems
- Persistently make progress
Verbal and writing communication skills
Knowledge in a specific domain
Mathematical foundations
Implementation skills
Insight and vision

Go over The Missing Semester of Your CS Education: command-line interface, shell script, zsh, vim, git, ssh, tmux
Python
- Use a virtual environment (uv or conda) for each project
- Use a code formatting tool for easy collaboration: Black is simple yet effective (ruff looks good too)
- Wrap your code as a package for reusability: How to manage a project code
English
- You don’t have to be fluent (I’m not fluent either). But, you should be able to communicate in English, especially speaking.
Some useful materials: Machine learning, probabilistic graphical model, deep learning or deep learning for computer vision, deep reinforcement learning, reinforcement learning theory, RL implementation, deep unsupervised learning, transformer, deep multi-task and meta learning, artificial intelligence explained by Korean neuroscientist, language modeling from scratch, flow matching and diffusion models
- Try assignments in JAX (recommended) or PyTorch
Learn LaTeX, a programming language for paper writing, and make your academic CV
Learn about basic web development (html, css, javascript) and build your academic website

RL Implementation
- Goal: Implement several state-of-the-art methods, do some experiments, and visualize results
- Try the packages frequently used in our lab: wandb (logging), tqdm (progress bar), matplotlib, seaborn (visualization), hydra-core (configuration), einops (tensor manipulation)
- Log all experiments and analysis using “reports” in wandb
- Always run experiments with multiple seeds (at least 3) — One good or bad result from a single seed may mean nothing
Simulated environments
- Goal: Understand the code structure of environments and try some of them
- Gymnasium (previously, OpenAI gym) is the most commonly used interface for RL
- Gymnasium, mujoco, dm_control, Isaac Lab, IKEA furniture assembly environment, FurnitureSim, and more
👍 Read papers from our lab
Read relevant papers

Robot learning reading list
Think about problems you want to solve and make an idea slide deck

Thinking about a research problem

Be proactive, participate in meetings, ask questions, think about problems even if not explicitly asked for
Actively work to get hands-on experience in all parts of the research pipeline: build slide decks to outline ideas, present in group meetings, write paper idea abstracts, help in implementing prototypes and running experiments
Ask questions sooner rather than later when you get stuck
Pair with a PhD student on your first project to maximize your learning curve
Work in person and stay in the lab as much as possible to interact with people (e.g. lunch, random discussion) and understand how people work in the lab