$ cat wiki/people/jim-fan.md
Jim Fan
Overview
NVIDIA Senior Research Scientist. A leading researcher in Embodied AI / Foundation Agent. Leads Project GR00T (humanoid robot foundation model). High signal frequency at the intersection of agents + robotics — 1.2x weight.
Affiliations
- NVIDIA (Senior Research Scientist, AI Agents)
Notable Recent Statements
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2026-06-17: ENPIRE — agentic robot policy self-improvement in the real world — Jim Fan's NVIDIA GEAR Lab (with CMU, UC Berkeley) released ENPIRE: a fleet of 8 real robots autonomously runs its own research loop — no human researchers in the loop. Results: 99% pass@8 on contact-rich tasks; physical scaling law discovered (8 parallel robots → superlinear policy improvement). Fan: "AutoResearch in the physical world for the first time." Builds directly on EgoScale (June 7): EgoScale → acquire skills from human video; ENPIRE → autonomously improve those skills. → ENPIRE: Agentic Robot Policy Self-Improvement in the Real World (source) (arXiv)
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2026-06-07: EgoScale — humanoid dexterous manipulation from egocentric human video — Announced that his NVIDIA team trained a humanoid with 22-DoF dexterous hands (Sharpa Wave tactile + Unitree H2 Plus, 75 DoF total) to assemble model cars, operate syringes, sort cards, and fold shirts — all learned from 20,000+ hours of egocentric human video with no robot teleoperation. Key finding: log-linear scaling law (R² = 0.998) between human video volume and real-robot task success rate — the strongest signal yet that LLM-era scaling extends to dexterous manipulation. Also demonstrated live VR teleoperation (PICO headset → Unitree G1). Open-sourced everything. (source)
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2026-04: CaP-X open-sourced — "Vibe agents alive in the physical world", robot arms + humanoids, perception/actuation APIs, auto-synthesize skill libraries (source)
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Foundation Agent roadmap: a single model learns to act across diverse virtual/physical worlds — Project GR00T as the cornerstone
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No-gradient architecture stance: "LLM acts as 'prefrontal cortex' that orchestrates lower-level control APIs via code generation" (citing Voyager)
Key Themes
- Embodied AI / robotics
- Foundation agents (cross-environment)
- LLM as high-level controller, with low-level control kept separate
- Open-source first
Why Tracked
- Represents the agentic robotics direction of NVIDIA (TBD)
- High frequency of agents matches + real-world application signals
- Intellectual parallel to Karpathy's AI-native software vision (abstracting humans-environments)
Related
- Embodied Agents
- Agentic Reinforcement Learning (indirect)
- Project GR00T page TBD