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RL agents can learn more robust vision-and-language navigation policies by exploring diverse trajectories and comparing their performance, even without expert demonstrations or value networks.
Ditch discrete waypoints: VLA models can now generate smooth, physically plausible robot trajectories by directly regressing continuous action functions.
By learning to project actions onto a low-dimensional manifold, ABot-M0 achieves faster and more stable robotic control policies compared to directly predicting actions in the full high-dimensional space.
