Delta-0: Why Humanoid Foundation Models Need Correctable Control

What makes a humanoid foundation model useful when its first attempt goes wrong? This episode examines how Delta-0 connects whole-body control, human-motion pretraining, and corrective learning—and what its experiments establish. We cover three questions: how its latent world-action model connects observations with motion commands; why relative end-effector corrections make human intervention easier; and what to make of the reported dishwasher success increase from 20% to 65% after targeted post-training. ([deltai.com](https://deltai.com/en/blog/delta-0)) The system runs on a Unitree G1 with WUJI hands, combining simulation-trained control with real-world household manipulation. ([robopapers.substack.com](https://robopapers.substack.com/p/ep115-delta-0-a-new-chapter-in-humanoid)) We place the approach alongside SONIC's shared whole-body control interface, HIL-SERL's intervention-driven reinforcement learning, and Zero-HLM's simulation-generated humanoid experience. ([nvlabs.github.io](https://nvlabs.github.io/GEAR-SONIC/)) The central limitation is generalization evidence: improvement after collecting corrections in a rearranged kitchen does not establish broad transfer across unfamiliar tasks and environments. ([deltai.com](https://deltai.com/en/blog/delta-0)) For roboticists building loco-manipulation policies, the practical question is whether the controller makes useful corrections easy to express and learn from. Could that interface matter as much as the policy architecture?