QuickLAP combines physical corrections with LLM-parsed natural language in a closed-form Bayesian update, reducing reward-learning error in simulated driving and improving user ratings in a 15-person study.
Learning a prior over intent via meta-inverse reinforcement learning
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
Learning the demonstrator's planning algorithm via a differentiable planner improves IRL reward inference over incorrect bias assumptions but underperforms exact planners.
citing papers explorer
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QuickLAP: Quick Language-Action Preference Learning for Semi-Autonomous Agents
QuickLAP combines physical corrections with LLM-parsed natural language in a closed-form Bayesian update, reducing reward-learning error in simulated driving and improving user ratings in a 15-person study.
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On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference
Learning the demonstrator's planning algorithm via a differentiable planner improves IRL reward inference over incorrect bias assumptions but underperforms exact planners.