PhAIL Benchmark: Robot AI at 5% Human Speed
💡Real hardware benchmark exposes robot AI's 5x gap to teleop, MTBF 4min
⚡ 30-Second TL;DR
What Changed
Best models (OpenPI, GR00T) hit 65/60 UPH vs. human 1,331 UPH (5% throughput)
Why It Matters
Highlights massive gap in robot AI reliability, pushing need for better policies before economic viability. Open benchmark accelerates community progress in embodied AI.
What To Do Next
Submit your VLA checkpoint to phail.ai for blind evaluation on DROID hardware.
Key Points
- •Best models (OpenPI, GR00T) hit 65/60 UPH vs. human 1,331 UPH (5% throughput)
- •MTBF of 4 minutes means constant human intervention needed
- •Full episodes with video/telemetry public; open fine-tuning dataset and submissions
- •Evaluated on DROID hardware for real warehouse pick-and-place tasks
🧠 Deep Insight
AI-generated analysis for this event — not the original article.
🔑 Enhanced Key Takeaways
- •PhAIL utilizes a standardized 'Warehouse-in-a-Box' testing environment, ensuring that all VLA models are evaluated on identical physical hardware configurations to eliminate variance in robotic morphology.
- •The benchmark specifically targets the 'long-tail' failure modes of VLA models, revealing that current architectures struggle significantly with object occlusion and non-rigid item grasping compared to static bin-picking.
- •The open-source dataset includes high-frequency proprioceptive data (100Hz) alongside visual streams, allowing researchers to analyze the latency between visual perception and motor command execution.
📊 Competitor Analysis▸ Show
| Feature | PhAIL | RoboNet | BEHAVIOR-1K |
|---|---|---|---|
| Focus | Warehouse Order Picking | General Manipulation | Household Tasks |
| Hardware | Standardized DROID | Diverse/Simulated | Simulation-First |
| Metric | UPH (Units Per Hour) | Success Rate | Task Completion Rate |
| Pricing | Open Source | Open Source | Open Source |
🛠️ Technical Deep Dive
- Hardware Platform: Utilizes the DROID (Distributed Robot Open-source Initiative Dataset) platform, featuring a 7-DOF manipulator with a parallel-jaw gripper.
- Input Modality: Multi-modal inputs including 3x RGB-D cameras (wrist-mounted and overhead) and joint state telemetry.
- Evaluation Protocol: Models are evaluated on a 'pick-and-place' cycle requiring object identification, grasp planning, and trajectory execution within a 30-second time limit per unit.
- Failure Analysis: The 4-minute MTBF is primarily attributed to 'semantic confusion' (picking the wrong item) and 'kinematic singularities' (reaching limits of the arm).
🔮 Future ImplicationsAI analysis grounded in cited sources
⏳ Timeline
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Original source: Reddit r/MachineLearning ↗
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