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Multimodal foundation models offer a promising framework for robotic perception and planning by processing sensory inputs to generate actionable plans.
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How can we know when language models know? on the calibration of language models for question answering
Jiang, Z., Araki, J., Ding, H., and Neubig, G · 2021
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Learning transferable visual models from natural language supervision
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Real-time uncertainty estimation in computer vision via uncertainty-aware distribution distillation
Shen, Y., Zhang, Z., Sabuncu, M. R., and Sun, L · 2021
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Do as I can, not as I say: Grounding language in robotic affordances
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Robots that ask for help: Uncertainty alignment for large language model planners
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Shah, D., Osinski, B., Ichter, B., and Levine, S · 2022
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
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Automaton-based representations of task knowledge from generative language models
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Multimodal pretrained models for verifiable sequential decision-making: Planning, grounding, and perception
Yang, Y., Neary, C., and Topcu, U · 2023
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Uncertainty estimation in deterministic vision transformer, 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
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Online fine-tuning with uncertainty quantification for offline pre-trained agents
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Direct preference optimization: Your language model is secretly a reward model
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Fine-tuning with uncertainty-aware priors makes vision and language foundation models more reliable
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On the planning abilities of openai’s o1 models: Feasibility, optimality, and generalizability
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Fine-tuning language models using formal methods feedback: A use case in autonomous systems
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