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UniMPA, a Unified Memory-Prediction-Action model that addresses transition ambiguity by modeling the intended future state evolution through a shared action-grounded transition interface, is proposed.
LookAgain's innovative closed-loop approach allows for real-time refinement of GUI grounding predictions, drastically improving accuracy on difficult tasks.
Mamba strikes again, enabling VLA models to learn more robust manipulation policies that generalize better to real-world scenarios and require less training data.
Robotic manipulation gets a serious upgrade: ConsisVLA-4D boosts performance by up to 41.5% and speeds up inference by 2.4x, all while ensuring your robot understands the scene in 3D *and* how it changes over time.
