RoboTTT: Context Scaling for Robot Policies
Recent robot foundation models operate with single-step or short-history visuomotor context. We introduce Test-Time-Training Robot Policies (RoboTTT), a robot model and training recipe that scale visuomotor context to 8K timesteps, three or
https://arxiv.org/abs/2607.15275v1 ↗Thesis fit
Good fit
Within your typical scope; diligence still required.
In your usual scope
Idea match
Light
How close the company’s idea is to your thesis statement
Sector
ai
Overlap with sectors you care about
Geography
Unknown
Location unknown — scores 0
Your thesis: “We back exceptional technical founders building AI-first products and infrastructure, deploying $100K checks within 24 hours.”
Founder ↑ improving
Traction → stable
Idea vs market ↓ declining
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Correct facts used on the next screening or memo.
People
Y
Yunfan Jiang
2.0
low confidence
Y
Yevgen Chebotar
7.0
low confidence
R
Ruijie Zheng
2.0
low confidence
F
Fengyuan Hu
2.0
low confidence
Y
Yunhao Ge
2.0
low confidence
J
Jimmy Wu
2.0
low confidence
T
Tianyuan Dai
2.0
low confidence
S
Scott Reed
2.0
low confidence
L
Li Fei-Fei
2.0
low confidence
Y
Yuke Zhu
2.0
low confidence
L
Linxi "Jim" Fan
1.0
low confidence
L
Linxi Fan
1.0
low confidence
Activity & evidence
Similar baseline plays (YC · idea space)
Cortex AI
· Active
Large-scale real-world robot & human data for embodied AI
founders not scraped yet