5 papers
ConformalNL2LTL: Translating Natural Language Instructions into Temporal Logic Formulas with Conformal Correctness Guarantees
David Smith Sundarsingh, Jun Wang, Jyotirmoy V. Deshmukh +1
Linear Temporal Logic (LTL) is a widely used task specification language for autonomous systems. To mitigate the significant manual effort and expertise required to define LTL-enco…
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…
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…
Mission-driven Exploration for Accelerated Deep Reinforcement Learning with Temporal Logic Task Specifications
Jun Wang, Hosein Hasanbeig, Kaiyuan Tan +2
This paper addresses the problem of designing control policies for agents with unknown stochastic dynamics and control objectives specified using Linear Temporal Logic (LTL). Recen…
Probabilistically Correct Language-based Multi-Robot Planning using Conformal Prediction
Jun Wang, Guocheng He, Yiannis Kantaros
This paper addresses task planning problems for language-instructed robot teams. Tasks are expressed in natural language (NL), requiring the robots to apply their capabilities at v…