Teach and Grow: An Agent-Centered Architecture for General Robot Learning

Chang Nie, Zhe Liu and Hesheng Wang are with the School of Automation and Intelligent Sensing at Shanghai Jiao Tong University and the Shanghai Key Laboratory of Navigation and Location Based Services, Shanghai 200240, China. The corresponding author is Hesheng Wang.

This paper is an arXiv preprint, intended for future submission to a journal or conference. The paper is on arXiv as arXiv:2608.17209, first posted 17 August 2026, with DOI 10.48550/arXiv.2608.17209.

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. Our implementation uses OpenAI GPT-6 Astra for multimodal reasoning and Codex to connect the AI Agent to robot tools. The AI Agent identifies subgoals shared across demonstrations, expresses them as closed-loop Skill Blocks, and grounds each block in the current scene. Physical feedback guides the next action and any recovery. Verified behaviors enter a persistent Skill Library; Experience Memory records the conditions and repairs that inform later decisions. TGL reaches 99.9% mean success on four LIBERO suites and 92.4% on seven LIBERO-Plus perturbation categories. Controlled studies show that taught blocks persist and improve related-task execution under the same model weights and executors. We further formulate a scaling hypothesis that relates effective reusable experience to falling future-task error and teaching demand. Code and demonstration videos: https://tgl.changnie.top.

Teach and Grow, TGL, Teach-and-Grow Learning, training-free robot learning, robot learning without fine-tuning, AI Agent-centered architecture, agentic robotics, AI Agent robot, Skill Blocks, Skill Library, Experience Memory, retraining tax, few-shot teaching, sparse teaching, robot manipulation, vision-language-action, VLA, world-action model, WAM, embodied AI, embodied intelligence, lifelong learning, continual learning, skill composition, GPT-6 Astra, Codex, LIBERO, LIBERO-Plus, robot foundation model, Franka, manipulation benchmark, test-time reasoning robot, Training-free robot learning, vision-language-action models

arXiv: 2608.17209 · DOI: 10.48550/arXiv.2608.17209 · PDF: arXiv:2608.17209 · Code: IRMVLab/TGL

BibTeX is available at /cite.bib under the key nie2026teachandgrow, and as CITATION.cff for reference managers. A plain-text citation is: Chang Nie, Zhe Liu and Hesheng Wang, “Teach and Grow: An Agent-Centered Architecture for General Robot Learning,” arXiv:2608.17209, 2026. DOI: 10.48550/arXiv.2608.17209.

The paper is released together with a reference implementation and ten paired demonstration videos. The project page covers the problem framing, the Skill Block architecture, a worked example, the controlled studies and the demonstrations.