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Qualitative examples show the PhysBrain 1.5 model's ability to produce end-effector trajectories and predict future scenes through spatially aligned RGB, depth, and robot-mask outputs.
IntentVLA is introduced, a history-conditioned VLA framework that encodes recent visual observations into a compact short-horizon intent representation and uses it to condition chunk generation and improves rollout stability and outperforms strong VLA baselines.
SIEVE, a structure-aware data selection method for VLA imitation learning that can surpass full-data training while using only 50% of demonstrations and 50% of training steps, suggests that reusable structure, captured through primitives and transitions, is an important signal for efficient VLA imitation learning.
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