An offline approach to fNIRS-guided reinforcement learning for robot behavior
Human-in-the-loop Reinforcement Learning has become a popular approach to training, finetuning, and aligning robot behavior with user preferences. Our paper explores the feasibility of using brain signals via functional near-infrared spectr
https://arxiv.org/abs/2607.14393v1 ↗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 → stable
Traction → stable
Idea vs market ↓ declining
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Correct facts used on the next screening or memo.
People
J
Julia Santaniello
2.0
low confidence
M
Madelaine Brower
2.0
low confidence
B
Benson Jiang
2.0
low confidence
D
Donatello Sassaroli
2.0
low confidence
R
Robert Jacob
2.0
low confidence
J
Jivko Sinapov
2.0
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