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Found 4 papers, 1 papers with code
Finetuning Generative Trajectory Model with Reinforcement Learning from Human Feedback
To address this, we introduce TrajHF, a human feedback-driven finetuning framework for generative trajectory models, designed to align motion planning with diverse driving preferences.
VR-Robo: A Real-to-Sim-to-Real Framework for Visual Robot Navigation and Locomotion
Recent success in legged robot locomotion is attributed to the integration of reinforcement learning and physical simulators.
AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review
However, prostate segmentation is challenging due to imperfections in the images and the prostate's complex tissue structure.
Large Trajectory Models are Scalable Motion Predictors and Planners
STR reformulates the motion prediction and motion planning problems by arranging observations, states, and actions into one unified sequence modeling task.
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