SMoDP routes action chunks in a diffusion policy to semantically specialized experts via a VLM-supervised skill predictor and dual contrastive alignment, achieving better efficiency and compositional transfer than baselines.
Moe-dp: An moe-enhanced diffusion policy for robust long-horizon robotic manipulation with skill decomposition and failure recovery
4 Pith papers cite this work. Polarity classification is still indexing.
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DeLock mitigates lock-in in low-data VLA post-training via visual grounding preservation and test-time contrastive prompt guidance, outperforming baselines across eight evaluations while matching data-heavy generalist policies.
MoRI dynamically mixes RL and IL experts with variance-based switching and IL regularization to reach 97.5% success in four real-world robotic tasks while cutting human intervention by 85.8%.
citing papers explorer
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Semantically Structured Mixture-of-Experts for Compositional Robotic Manipulation
SMoDP routes action chunks in a diffusion policy to semantically specialized experts via a VLM-supervised skill predictor and dual contrastive alignment, achieving better efficiency and compositional transfer than baselines.
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Breaking Lock-In: Preserving Steerability under Low-Data VLA Post-Training
DeLock mitigates lock-in in low-data VLA post-training via visual grounding preservation and test-time contrastive prompt guidance, outperforming baselines across eight evaluations while matching data-heavy generalist policies.
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MoRI: Mixture of RL and IL Experts for Long-Horizon Manipulation Tasks
MoRI dynamically mixes RL and IL experts with variance-based switching and IL regularization to reach 97.5% success in four real-world robotic tasks while cutting human intervention by 85.8%.
- GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization