Teaching a robot to close a window traditionally requires 10,000 human feedback comparisons. That's three days of tedious labor—per task. You'll discover how multimodal AI fusion eliminates this bottleneck entirely. Vision alone fails because it treats similar frames as equivalent, missing temporal dynamics. Language alone hallucinates success based on commands. But together, with smart conflict resolution using Probabilistic Soft Logic, they become reliable synthetic teachers. The result? Zero-shot robotics training. No human labels required. This is how foundation models are finally making robotics scalable.


Topics Covered
- The human-in-the-loop bottleneck (10,000 labels per task!)
- Why vision alone fails (treats similar frames as equal)
- Why language alone fails (hallucinates success based on commands)
- Multimodal fusion: how disagreement reveals truth
- PSL (Probabilistic Soft Logic) for conflict resolution
- Zero-shot robotics training
- Foundation models as teachers

Robots That Learn Without Humans

This podcast series cuts through AI hype to deliver what technical professionals actually need: honest assessments of what works, what fails, and why it matters. Curated from NeurIPS 2025 — the world's premier AI research conference — these 11 episodes translate cutting-edge research into accessible narratives without dumbing down the substance.

Basisset.com

Robots That Learn Without Humans

NeurIPS 2025 by Basis Set