3 citations · 9 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide Exploration
Yiannis Kantaros, Jun Wang
This paper addresses the problem of learning optimal control policies for systems with uncertain dynamics and high-level control objectives specified as Linear Temporal Logic (LTL)…
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…
Verified Compositional Neuro-Symbolic Control for Stochastic Systems with Temporal Logic Tasks
Jun Wang, Haojun Chen, Zihe Sun +1
Several methods have been proposed recently to learn neural network (NN) controllers for autonomous agents, with unknown and stochastic dynamics, tasked with complex missions captu…
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…