SO-TA replaces standard attention with optimal-transport alignment across vision, force/torque, and proprioception to improve diffusion-policy performance on real-robot insertion and wiping tasks.
Deep generative models in robotics: A survey on learning from multimodal demonstrations,
4 Pith papers cite this work. Polarity classification is still indexing.
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SCDP adds lightweight scene encoding and ambiguity detection to pre-trained diffusion policies so they produce legible motions only when goals are unclear and efficient motions otherwise.
CLARE is an exemplar-free continual learning framework for VLAs that autonomously expands modular adapters based on feature similarity and uses autoencoder routing for label-free deployment.
CLIC uses set-valued action targets from interactive human corrections instead of pointwise labels to train more robust imitation learning policies.
citing papers explorer
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Spacetime Optimal-Transport Attention for Visuo-Haptic Imitation Learning of Contact-Rich Manipulation
SO-TA replaces standard attention with optimal-transport alignment across vision, force/torque, and proprioception to improve diffusion-policy performance on real-robot insertion and wiping tasks.
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Encoding Predictability and Legibility for Style-Conditioned Diffusion Policy
SCDP adds lightweight scene encoding and ambiguity detection to pre-trained diffusion policies so they produce legible motions only when goals are unclear and efficient motions otherwise.
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CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion
CLARE is an exemplar-free continual learning framework for VLAs that autonomously expands modular adapters based on feature similarity and uses autoencoder routing for label-free deployment.
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From Action Labels to Sets: Rethinking Action Supervision for Imitation Learning from Corrective Feedback
CLIC uses set-valued action targets from interactive human corrections instead of pointwise labels to train more robust imitation learning policies.