ARP enhances quantized skill abstractions in imitation learning by coupling visual grounding via contrastive alignment with execution refinement via IRH, reporting SOTA results on LIBERO, Meta-World, and real-robot tasks.
H-gap: Humanoid control with a generalist planner.arXiv preprint arXiv:2312.02682
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Stellar VLA achieves continual learning in VLA models by maintaining a growing knowledge space and routing tasks to specialized experts conditioned on semantic relations, delivering strong LIBERO benchmark results with only 1% data replay and successful real-world transfer on dual-arm hardware.
Extracting task vectors from offline data to define training task distributions improves zero-shot offline RL performance by an average of 20%.
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Improving Zero-Shot Offline RL via Behavioral Task Sampling
Extracting task vectors from offline data to define training task distributions improves zero-shot offline RL performance by an average of 20%.