Teach and Grow: An Agent-Centered Architecture for General Robot Learning
Vision-language-action (VLA) and world-action models typically absorb unfamiliar manipulation tasks through additional robot data collection and policy optimization. This recurring retraining burden slows the acquisition of new behavior. We present Teach-and-Grow Learning (TGL), a training-free architecture that turns a few successful demonstrations into reusable robot skills. Task acquisition requires no gradient updates, fine-tuning, or reinforcement learning: pretrained model weights remain fixed as the robot expands its explicit knowledge. Teaching is an accelerator, not a precondition, because the agent can also drive the robot directly, and demonstrations mainly improve reliability....
| Authors | Chang Nie, Zhe Liu, Hesheng Wang |
|---|---|
| Primary category | cs.RO |
| Published | 17 August 2026 |
| Last updated at source | 20 September 2026 |
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