AI RESEARCH
AtomicVLA: Unlocking the Potential of Atomic Skill Learning in Robots
arXiv CS.CV
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ArXi:2603.07648v1 Announce Type: cross Recent advances in Visual-Language-Action (VLA) models have shown promising potential for robotic manipulation tasks. However, real-world robotic tasks often involve long-horizon, multi-step problem-solving and require generalization for continual skill acquisition, extending beyond single actions or skills. These challenges present significant barriers for existing VLA models, which use monolithic action decoders trained on aggregated data, resulting in poor scalability.