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cs.RO2025
CoFineLLM: Conformal Finetuning of LLMs for Language-Instructed Robot Planning
Jun Wang, Yevgeniy Vorobeychik, Yiannis Kantaros
Large Language Models (LLMs) have recently emerged as planners for language-instructed agents, generating sequences of actions to accomplish natural language tasks. However, their…
cs.RO2025
Plug-and-Play Physics-informed Learning using Uncertainty Quantified Port-Hamiltonian Models
Kaiyuan Tan, Peilun Li, Jun Wang +1
The ability to predict trajectories of surrounding agents and obstacles is a crucial component in many robotic applications. Data-driven approaches are commonly adopted for state p…