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Found 5 papers, 1 papers with code
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations
Grounding the common-sense reasoning of Large Language Models in physical domains remains a pivotal yet unsolved problem for embodied AI.
Human-Machine Cooperative Multimodal Learning Method for Cross-subject Olfactory Preference Recognition
Secondly, a complementary multimodal data mining strategy is proposed to effectively mine the common features of multimodal data representing odor information and the individual features in olfactory EEG representing individual emotional information.
Improving Small Language Models on PubMedQA via Generative Data Augmentation
Large Language Models (LLMs) have made remarkable advancements in the field of natural language processing.
Visual Pre-training for Navigation: What Can We Learn from Noise?
One powerful paradigm in visual navigation is to predict actions from observations directly.
Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations
In this work, we identify the roots of this challenge as the failure of a learned continuous policy to satisfy the discrete plan implicit in the demonstration.
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