A hypernetwork generates complete task-specific visuomotor policy parameters from instructions alone to structurally eliminate observation leakage in language-conditioned robotic control.
Efficient diffusion transformer policies with mixture of expert denoisers for multitask learning.arXiv preprint arXiv:2412.12953, 2024
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A Bayesian expert selection framework with variational Bayesian last layers and lower confidence bounds improves diffusion policies for active multi-target tracking.
Supervised MoE on top of ACT achieves higher success in bowel grasping/retraction from <150 demos than standard ACT or generalist VLAs, with OOD robustness, unseen viewpoint generalization, and zero-shot ex vivo porcine transfer.
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.
citing papers explorer
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DISC: Decoupling Instruction from State-Conditioned Control via Policy Generation
A hypernetwork generates complete task-specific visuomotor policy parameters from instructions alone to structurally eliminate observation leakage in language-conditioned robotic control.
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Diffusion Policy with Bayesian Expert Selection for Active Multi-Target Tracking
A Bayesian expert selection framework with variational Bayesian last layers and lower confidence bounds improves diffusion policies for active multi-target tracking.
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Supervised Mixture-of-Experts for Surgical Grasping and Retraction
Supervised MoE on top of ACT achieves higher success in bowel grasping/retraction from <150 demos than standard ACT or generalist VLAs, with OOD robustness, unseen viewpoint generalization, and zero-shot ex vivo porcine transfer.
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Continually Evolving Skill Knowledge in Vision Language Action Model
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.