PhAIL
Real-robot benchmark for AI models
Details
- External ID
- 47589797
- Source
- HN
- Company
- —
- Product
- PhAIL
- Website domain
- phail.ai
- Launched
- March 31, 2026
- Cohort
- —
- Upvotes
- 21
- Upvotes percentile
- 0.7503075030750308
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
I built this because I couldn't find honest numbers on how well VLA models [1] actually work on commercial tasks. I come from search ranking at Google where you measure everything, and in robotics nobody seemed to know.PhAIL runs four models (OpenPI/pi0.5, GR00T, ACT, SmolVLA) on bin-to-bin order picking – one of the most common warehouse operations. Same robot (Franka FR3), same objects, hundreds of blind runs. The operator doesn't know which model is running.Best model: 64 UPH. Human teleoperating the same robot: 330. Human by hand: 1,300+.Everything is public – every run with synced video and telemetry, the fine-tuning dataset, training scripts. The leaderboard is open for submissions.Happy to answer questions about methodology, the models, or what we observed.[1] Vision-Language-Action: https://en.wikipedia.org/wiki/Vision-language-action_model
Enrichment
- Theme
- embodied AI and robotics platforms
- Vertical
- Horizontal
- Function
- Observability & eval
- Audience
- Developer
- AI stance
- Not AI
- Project type
- Commercial product
- Normalized one-liner
- benchmark testing for ai models using real robots
- Manually corrected
- False
Could you build this?
No PhAIL evaluates Vision-Language-Action (VLA) models on real physical robotic hardware (Franka FR3 + Robotiq gripper) across commercial manipulation tasks. Physical robot integration, real-world hardware test environments, and physical benchmark automation cannot be vibe-coded.
What it would actually take: A real implementation requires physical robotic hardware (e.g., Franka Emika FR3 arms, Robotiq grippers, calibrated camera rigs), low-level robot control software (ROS2, real-time Linux kernels), and an automated physical resetting harness. The software pipeline runs inference for complex VLA models (OpenPI, GR00T, ACT) in real-time control loops, logs trajectory data, and computes standardized manipulation metrics. It demands deep robotics engineering, physical lab space, hardware maintenance, and vision-motor calibration expertise.
Discussion
8 comments analyzed.
Competitors mentioned: DreamZero (NVIDIA), Remote teleoperation solutions for robotics, Simulation-based benchmarks
Concerns raised: Speed of robot model performance gap narrowing, Real-world vs simulation performance discrepancy
Feature requests: Add more robot models to leaderboard, Benchmark for manipulation tasks specifically
Competitors
Other products that read as similar to this one — 99 launches clear the similarity bar, closest 8 shown.
Attention rank: #25 of 100 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 143 days after the earliest competitor.
- Compute:Arena · hn · 2026-09-17 · 5 upvotes · similarity 0.47
- benchboard · github · 2026-09-18 · 18 upvotes · similarity 0.42
- ManiLoop · github · 2026-09-16 · 12 upvotes · similarity 0.41
- The AI Leaderboard · ph · 2026-09-11 · 1 upvotes · similarity 0.41
- Run open-weight OCR, VLM and vision models behind one API · hn · 2026-09-04 · 5 upvotes · similarity 0.40
- Slop or not · hn · 2026-03-12 · 19 upvotes · similarity 0.39
- One Robot - World models for robots · yc · 2026-02-25 · 18 upvotes · similarity 0.39
- ai-model-rankings · github · 2026-09-22 · 16 upvotes · similarity 0.39
Other launches for this product
- No other launches for this product.
Same idea, different domain
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