From Jim Fan · NVIDIA GEAR
Robots that think
fast and slow
Kahneman's two systems map cleanly onto a robot's brain: a slow, deliberate planner and a fast, intuitive body. How the two talk to each other is still open research.
The thesis
Plan at one hertz. Act at a thousand. A robot mind needs both tempos at once.
Borrowing from Daniel Kahneman's Thinking, Fast and Slow, Jim Fan splits embodied intelligence in two. System 2 deliberates: reasoning, planning, and writing code. Large models already do this well. System 1 executes: fast, intuitive motor control that never reaches conscious thought. Grasping a cup, you do not decide where each fingertip goes at every millisecond.
System 2
Deliberate: reason, plan, write code.
The slow mind runs on big models. Vision-language models and LLMs in a loop can already reason about a scene, decompose a task, and even generate code to orchestrate behavior, deciding what to do next at roughly one decision per second.
- Superpower
- Long-horizon reasoning
- Blind spot
- Seconds of latency
A model that thinks in seconds cannot close a control loop that physics demands in milliseconds.
System 1
Intuitive: act before you can explain it.
The fast mind is reflex. Whole-body balance, grasping, and contact-rich manipulation need decisions at something like a thousand hertz. This points to compact, fast sensorimotor policies rather than giant models.
- Superpower
- Reflex-speed control
- Blind spot
- No deliberation
A reflex cannot plan a multi-step task or reason its way through a situation it has never seen.
The trade space
Two tempos, one body.
| System | Tempo | Mode | Runs on | Breaks when |
|---|---|---|---|---|
| System 2 | Deliberate | Large VLMs & LLMs in a loop | Contact needs millisecond reflexes | |
| System 1 | Intuitive | Compact sensorimotor policies | The task needs long-horizon planning |
The open problem
The interface is the research.
Fan frames the unsolved part as architecture: one monolithic model or a cascade of specialized ones. If it is a cascade, do the systems communicate through text or through latent variables? Text is interpretable but slow and lossy; latents are rich but opaque. NVIDIA's later GR00T N1 made the framing concrete: a vision-language module for System 2 driving a diffusion-transformer action module for System 1.
-
01
MonolithicOne end-to-end model: cleaner, harder to control
-
02
CascadedSeparate models per system, wired together
-
03
The wireText or latent vectors between the systems?
-
04
The clockBridging 1 Hz deliberation and 1 kHz control
Strategic implication
Own the reflexes. Rent the reasoning.
General-purpose System 2 reasoning is fast becoming a commodity available from foundation-model providers. The defensible layers for a robot maker are System 1, with embodiment-specific, safety-critical, kilohertz-rate control, and the interface contract between the two systems.
That contract is also a safety boundary: while System 2 deliberates for seconds, System 1 must stay competent and safe on its own. Designing the handshake, including skill APIs, latent commands, and interruption semantics, is product work, not just research.
System 2 sets the goal. System 1 does the touching. The handshake is still research.