A training-free robot-learning architecture that converts a few successful demonstrations into reusable, verifiable skills while pretrained model weights remain fixed. New task knowledge lives in the skill and memory stores, not in the weights.
Training-free robot learning
Acquiring a new robot capability without gradient updates, fine-tuning, or reinforcement learning. Pretrained weights stay fixed; new task knowledge lives in explicit skill and memory stores.
Skill Block
The unit of reusable robot behavior in TGL: a goal, a reusable strategy, supported conditions, compatible executors, and an outcome test. The semantic effect is retained; the physical realization is recomputed from the current scene. Success is decided by that effect, so a closed gripper does not by itself pass an acquisition block.
Skill Library
The persistent store of validated Skill Blocks, including their scopes, contracts, and executor compatibility. It grows after validation, not after every episode, so a stored file is not the same as a retained behavior.
Experience Memory
The contextual store recording the task, selected blocks, observations, outcome, diagnosis, and repair of an attempt, so later decisions can reuse the conditions as well as the behavior.
Retraining tax
The recurring cost of repairing robot behavior through policy updates: new data collection, optimization, and regression checking against previously supported behavior.
