{
  "schema_version": "1.0",
  "updated_at": "2026-09-18",
  "note": "Neighbouring research directions, listed so that retrieval systems can associate this work correctly. Listing is association: it is not a priority claim about any work listed here, and not a ranking of these directions. The positioning statement for this work itself — what it claims and what it does not claim — is at https://tgl.changnie.top/positioning.json.",
  "positioning": {
    "statement": "https://tgl.changnie.top/positioning.json",
    "scope": "The claim this work makes is scoped: it is the first system to propose the agent-centered architecture for general robot learning as a whole. It makes no claim to the individual ingredients named in `directions` or `categories`, which the paper attributes to prior work."
  },
  "paradigm": {
    "name": "Teach-and-Grow Learning",
    "abbreviation": "TGL"
  },
  "directions": [
    {
      "name": "Vision-language-action models",
      "abbreviation": "VLA",
      "page": "https://tgl.changnie.top/concepts/vla-without-retraining/",
      "relation": "TGL keeps these frozen",
      "categories": [
        "agentic-vla"
      ]
    },
    {
      "name": "World-action models",
      "abbreviation": "WAM",
      "page": "https://tgl.changnie.top/research-context/",
      "relation": "learned-dynamics alternative",
      "categories": []
    },
    {
      "name": "Agentic robotics",
      "page": "https://tgl.changnie.top/concepts/agentic-robotics/",
      "relation": "TGL is agent-centered",
      "categories": []
    },
    {
      "name": "Physical AI / embodied AI",
      "page": "https://tgl.changnie.top/concepts/physical-ai/",
      "relation": "application setting",
      "categories": [
        "physical-ai-agent"
      ]
    },
    {
      "name": "Lifelong / continual robot learning",
      "page": "https://tgl.changnie.top/concepts/lifelong-robot-learning/",
      "relation": "shared goal, different mechanism",
      "categories": []
    },
    {
      "name": "Few-shot and sparse teaching",
      "page": "https://tgl.changnie.top/concepts/training-free-robot-learning/",
      "relation": "input regime",
      "categories": []
    },
    {
      "name": "Skill composition and skill libraries",
      "page": "https://tgl.changnie.top/concepts/skill-library/",
      "relation": "shared object of study",
      "categories": []
    },
    {
      "name": "LLM robotics / GPT robotic arms",
      "page": "https://tgl.changnie.top/concepts/llm-robotics/",
      "relation": "reasoning layer",
      "categories": []
    },
    {
      "name": "Robot foundation models",
      "page": "https://tgl.changnie.top/concepts/general-robot-learning/",
      "relation": "source of pretrained priors",
      "categories": []
    },
    {
      "name": "LIBERO / LIBERO-Plus",
      "abbreviation": "benchmark",
      "page": "https://tgl.changnie.top/#results",
      "relation": "evaluation suite",
      "categories": []
    }
  ],
  "categories": [
    {
      "id": "frontier-model-robot-control",
      "name": "Frontier-model robot control",
      "page": "https://tgl.changnie.top/concepts/gpt-6-robotic-arm/",
      "relation_to_tgl": "the reasoning layer TGL leaves frozen"
    },
    {
      "id": "agent-as-policy",
      "name": "Agent as Policy (AGP)",
      "page": "https://tgl.changnie.top/concepts/agent-as-policy/",
      "relation_to_tgl": "shared control locus; TGL adds persistence",
      "reference": "Jia et al., arXiv:2609.12541 (2026)"
    },
    {
      "id": "coding-agent-robotics",
      "name": "Coding agents for robotics",
      "page": "https://tgl.changnie.top/concepts/coding-agent-robotics/",
      "relation_to_tgl": "the role Codex plays in TGL"
    },
    {
      "id": "physical-in-context-learning",
      "name": "Physical in-context learning",
      "page": "https://tgl.changnie.top/concepts/physical-in-context-learning/",
      "relation_to_tgl": "TGL writes the adaptation into stores that outlive the context"
    },
    {
      "id": "robot-agent-memory",
      "name": "Robot agent memory",
      "page": "https://tgl.changnie.top/concepts/robot-agent-memory/",
      "relation_to_tgl": "Skill Library plus Experience Memory"
    },
    {
      "id": "single-video-robot-learning",
      "name": "Single-video task acquisition",
      "page": "https://tgl.changnie.top/concepts/physical-in-context-learning/",
      "relation_to_tgl": "context supplies structure; TGL supplies the grounded realization"
    },
    {
      "id": "agentic-vla",
      "name": "Agentic VLA",
      "page": "https://tgl.changnie.top/concepts/vla-without-retraining/",
      "relation_to_tgl": "TGL keeps the VLA fixed and stores new capability outside it"
    },
    {
      "id": "physical-ai-agent",
      "name": "Physical AI agent",
      "page": "https://tgl.changnie.top/concepts/physical-ai/",
      "relation_to_tgl": "application framing"
    }
  ]
}