GPT and Robotic Arms
“GPT robotic arm” describes a robot arm controlled with the help of a large pretrained model. That happens at two levels: a language model can plan and sequence the task in words, and a vision-language-action model can drive the arm directly. Both work. Neither removes the need to check what the arm physically did.
Level one: planning
A language model can decompose a goal into steps, choose tools and recover from some failures, because that reasoning is largely symbolic. It has no access to joint angles and does not need them. This level is well established and mostly a software-integration problem.
Level two: direct action
A vision-language-action model takes images, an instruction and proprioception as input and outputs continuous actions. It works because the backbone's pretraining already produces aligned representations of scenes and language, so comparatively little robot data suffices to attach an action head.
What neither level supplies
A model can plan well and still be wrong about the world, because a plan is not evidence. Something has to observe the physical outcome and decide whether the intended effect actually occurred — a closed gripper is not proof that an object is held. That check is what turns a sequence of commands into a behaviour with a defined scope.
It is also what makes repair local. When the outcome is checked against a stated effect, a failure points at one behaviour rather than at an entire policy.
How TGL puts this together
Teach-and-Grow Learning uses a multimodal GPT-class AI Agent for task-level reasoning and tool interaction, and wraps each subgoal in a Skill Block with an outcome test. The AI Agent decides what should change; the robot-side executors decide how, and report back what happened.