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Authentic execution dynamics in multi-turn interactions can double the performance of OS agents, challenging the efficacy of larger models.
High-fidelity simulations in Real-IKEA reveal that robust manipulation strategies can be learned by prioritizing mechanical advantage over fragile friction-based methods.
FLASH enables robots to master complex deformable manipulation tasks in minutes using only synthetic data, eliminating the need for labor-intensive real-world training.
