Physical AI Lab — Launch Roadmap
Is Now a Good Time to Recruit?
Mid-semester is actually a smart time to recruit for next semester. Students are deep enough into their current work to know what they want more of, and you’ll have a pool ready to hit the ground running after break. Aim to have commitments locked in 3–4 weeks before semester’s end.
How Many Students & What Types
Start with 4–6 students total. Small enough to manage quality, large enough to run parallel projects. You want:
- 1–2 Hardware/Embedded folks — comfortable with microcontrollers, sensors, actuators (Arduino, Raspberry Pi, ROS). These are your build leads.
- 1–2 ML/AI people — computer vision, reinforcement learning, or edge inference. They bridge the model side to the physical side.
- 1 generalist engineer — someone who can write clean code, document well, and isn’t precious about switching tasks. Invaluable glue.
- 1 strong writer/researcher — even one person who can draft clearly pays dividends at paper time.
Mix of undergrad and grad if possible. Undergrads bring energy and time; grad students bring depth and ownership.
PI’s Role
Given your background, I’d suggest a shift: move from solo researcher with advisors to research director with active technical involvement in one thread. Concretely:
- Pick one project where you are genuinely in the weeds — designing experiments, analyzing results, writing sections. This keeps your skills sharp and gives students a model to emulate.
- On the other projects, be a structured advisor: weekly check-ins, sharp feedback on writeups, and blocking decisions (not bottlenecking execution).
- You write the framing and introduction sections of papers. Students often struggle most with situating work in the literature — this is where your experience adds the most value.
Midterm-to-Break Research Project Ideas
These are scoped to be completable in ~6–8 weeks and publishable at a workshop or short paper venue:
- Sim-to-Real Gap on a Budget — Train a simple manipulation or locomotion policy in simulation (Isaac Gym / MuJoCo), deploy on cheap hardware, and systematically measure the gap. Lots of venues want honest benchmarks like this.
- Edge Vision for Robotic Trigger Detection — Deploy a fine-tuned vision model (e.g. YOLO variant) on a Jetson Nano or Pi 5, and benchmark latency/accuracy tradeoffs under real-world lighting/occlusion. Great for a systems-flavored paper.
- Human-in-the-Loop Correction Study — Build a small robot task (pick-and-place, navigation) and study how few corrective demonstrations are needed to adapt a pretrained policy. Connects neatly to RLHF/HITL literature.
- Physical LLM Interface — Use an LLM (via API) as a task planner for a physical agent, and evaluate failure modes systematically. Very publishable right now given LLM-robotics interest.
- Tactile/Sensor Fusion Baseline — If you have any force or tactile sensors, even a basic dataset + baseline model contributes something concrete to an underexplored area.
Roadmap
| Phase | Timing | Actions |
|---|---|---|
| Seed | Now → 3 weeks | Draft lab mission statement; soft-recruit 2–3 students informally; sketch project list |
| Recruit | Weeks 3–6 | Post formal call; interview 8–10; select 4–6; assign to projects |
| Sprint | Midterms → Break | Focused project execution; weekly standups; draft paper outlines |
| Write | Over break | Paper drafts; you edit and frame; target workshop/short paper deadlines |
| Launch | Start of next semester | Lab site goes live; first blog post; submit first paper |
Online Presence — Content Suggestions
Site structure:
- About — Lab mission (1 clear paragraph: what problems, what approach, why physical AI)
- People — You + students with short bios and GitHub/Scholar links
- Research — Project cards with status (ongoing / submitted / published)
- Blog — Where most of your energy should go early on
Blog content to start with:
- “Why Physical AI, Why Now” — your founding statement
- “What We’re Building This Semester” — project previews, generates interest
- A build log post per project (students write these, you edit)
- “What We Learned Presenting at [Conference]” — you already have material for this
Recruiting email (to send to undergrad/grad lists):
Subject: Joining the [Lab Name] Physical AI Lab — Paid/Credit Research Positions
I’m forming a small research group focused on physical AI — where learned models meet real hardware. We’re working on problems involving robotic perception, edge inference, and human-robot interaction, with a goal of publishing at venues like [CoRL / ICRA / RA-L workshops / etc.].
I’m looking for 4–6 students (undergrad and MS) who are curious, self-directed, and want to put their name on real publications. Background in robotics, CV, or embedded systems is helpful but not required — motivation matters more.
If this sounds like you, send me a short email (not a formal CV) describing one thing you’ve built or studied that you’re proud of.
The single most important thing early on: ship one paper before the lab site has been live six months. Even a workshop paper signals that this is a real lab, not a landing page.