Concept index

Explore the architecture and its connections to robot learning.

• Teach-and-Grow Learning (TGL)

  Teach-and-Grow Learning (TGL) is a training-free architecture in which a pretrained AI Agent turns a few demonstrations into reusable, verifiable Skill Blocks while model weights stay fixed. New task knowledge lives in the Skill Library and Experience Memory.

• Training-Free Robot Learning

  Training-free robot learning means acquiring a new capability without gradient updates, fine-tuning or reinforcement learning. Pretrained weights stay fixed; new task knowledge lives in explicit skill and memory stores instead.

• Skill Block

  A Skill Block is the unit of reusable robot behaviour in Teach-and-Grow Learning: a goal, a reusable strategy, supported conditions, compatible executors and an outcome test. The semantic effect is retained; the physical realization is recomputed.

• Skill Library

  The Skill Library in Teach-and-Grow Learning is the persistent store of validated Skill Blocks: their goals, reusable strategies, supported conditions, compatible executors and outcome tests. It grows by validation, not by every episode.

• Experience Memory

  Experience Memory in Teach-and-Grow Learning records the context of an attempt — task, selected blocks, observations, outcome, diagnosis and repair — so later decisions can reuse the conditions, not only the behaviour.

• The Retraining Tax

  The retraining tax is the recurring cost of repairing robot behaviour through policy updates: new data collection, optimisation, and regression checking against everything the policy previously supported. Teach-and-Grow Learning names it and proposes an alternative.

• Agentic Robotics

  Agentic robotics puts a reasoning AI Agent in charge of selecting subgoals and tools while specialised components handle geometry and control. Teach and Grow is an AI Agent-centered architecture for general robot learning.

• General Robot Learning

  General robot learning aims for one system that handles many tasks and scenes. The dominant route scales data and parameters; Teach-and-Grow Learning takes a complementary route through explicit, reusable skills with fixed weights.

• Lifelong Robot Learning

  Lifelong robot learning means a robot keeps acquiring tasks across its working life. Parameter updates risk overwriting earlier competence; Teach-and-Grow Learning keeps new capability in explicit stores that do not overwrite anything.

• Adapting a VLA Model Without Retraining

  Vision-language-action models normally absorb a new task by collecting more data and updating the policy. Teach-and-Grow Learning asks what can be adapted while the VLA weights stay frozen, and stores the answer as explicit Skill Blocks.

• Physical AI and Embodied AI

  Physical AI and embodied AI describe systems that perceive and act in the physical world through sensors and actuators. For manipulation the real questions are sensory interfaces, timing under delayed control, and where training data comes from.

• LLM Robotics

  Large language models contribute task decomposition, tool selection and recovery to robotics, but they cannot emit control signals. Teach-and-Grow Learning uses a multimodal LLM AI Agent for reasoning and leaves execution to robot-side components.

• GPT and Robotic Arms

  A GPT-class model can plan and sequence a robotic arm's task in language, and a vision-language-action model can drive it directly. Neither removes the need for physical validation of what the arm actually did.

• AI Agent Robotic Arm

  An AI Agent robotic arm pairs a reasoning AI Agent with the perception, grasping and motion tools an arm needs. Teach-and-Grow Learning is an AI Agent-centered design of that pairing, with verification at every subgoal.

• GPT-6 Robotic Arm

  GPT-6-class robotic-arm systems use a frontier multimodal model as a reasoning layer that interprets visual observations and invokes robot-control tools or generated programs. Recent 2026 demonstrations use GPT-6 Astra for real robot-arm manipulation.

• Agent as Policy (AGP)

  Agent as Policy (AGP) puts a general-purpose AI Agent inside the robot's execution loop instead of limiting it to offline planning. Named and demonstrated by Jia et al., arXiv:2609.12541, 2026.

• Coding AI Agents for Robotics

  Coding AI Agents inspect state, call tools, write and run short programs, and read the result. That loop fits the boundary between a frontier model and a robot's control stack, and it is the role Codex plays in Teach-and-Grow Learning.

• Physical In-Context Learning

  Physical in-context learning lets a robot adapt to a new task from context — demonstrations, video, a written procedure — without updating model weights. TGL stores the reusable behaviour and experience persistently so learning accumulates across tasks.

• General-Purpose AI Agent Robots

  A general-purpose AI Agent robot uses one reasoning AI Agent across many tasks. Its openness is the point, and its statelessness is the limit — Teach-and-Grow Learning gives such an AI Agent persistent, inspectable memory.

• Robot Learning Without Retraining

  Robot learning without retraining means acquiring a new task without a policy update: no gradient step, no task-specific fine-tuning, no reinforcement-learning stage. New capability is stored explicitly instead of written into weights.

• Robot AI Agent Memory

  Robot-AI Agent memory preserves information from earlier physical interaction for future decisions. In TGL, a Skill Library stores reusable executable behaviour while Experience Memory carries forward success, failure, diagnosis and repair.

• Runtime Reasoning for Robots

  Runtime reasoning means the model that decides what to do next is still running while the task executes, so it can act on what the robot observes. It is the opposite of committing to an offline plan.

• Tool Use in Robotics

  Tool use in robotics means exposing perception, grasping and motion as callable capabilities an AI Agent selects and invokes. It is what lets a reasoning model act without emitting control signals itself.