Qiyue Dong — accepted AI-conference papers

2025

LATMOS: Latent Automaton Task Model from Observation Sequences

Weixiao Zhan, Qiyue Dong, Eduardo Sebastián, Nikolay Atanasov

IROS 2025

Robot task planning from high-level instructions is an important step towards deploying fully autonomous robot systems in the service sector. Three key aspects of robot task planning present challenges yet to be resolved simultaneously, namely, (i) factorization of complex tasks specifications into

Cited by 2 Source ↗code ↗Scholar ↗

2025

LTLCodeGen: Code Generation of Syntactically Correct Temporal Logic for Robot Task Planning

Behrad Rabiei, Mahesh Kumar A. R., Zhirui Dai, Surya L. S. R. Pilla, Qiyue Dong, Nikolay Atanasov

IROS 2025

This paper focuses on planning robot navigation tasks from natural language specifications. We develop a modular approach, where a large language model (LLM) translates the natural language instructions into a linear temporal logic (LTL) formula with propositions defined by object classes in a seman

Cited by 4 Source ↗Scholar ↗