Pretraining Knows What, Simulation Teaches How: Compositional Generalization in a Sim-Trained Robot Agent
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Benjamin Pikus, Daniel Saunders, Mircea Mironenco, Salman Mohammadi, Mathew Mathew, Sirou Zhu et al. · arXiv: 2610.35817v1 · Summary page
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A robot learns motion in simulation and meaning from pretraining
Zero-real Agent Policy, or ZAP, separates understanding a task from learning how to move. The recipe starts with a pretrained vision-language model, then uses supervised fine-tuning and reinforcement learning on simple simulated tasks to teach robot control through a tool interface. Without real-world robot training data, it reached 96.7% success on the authors' real-world tasks, matching GPT-6 Astra in their harness while running 3x faster and 17x cheaper.
The reported demonstrations included unfamiliar objects, goals and strategies, such as passing a test tube between arms and drawing on a tic-tac-toe board. Transfer to an unseen single-arm robot reached 55.9% on RoboLab-120. These results suggest that pretrained knowledge and simulated motor practice can contribute complementary capabilities. They do not establish unrestricted real-world competence: performance remained dependent on the evaluation setting, and transfer to the unseen embodiment was substantially less successful.
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