Author

Anastasios Manganaris

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Preprint Aug 2026

Composing Learned Robot Behaviors with Temporal Logic at Runtime

Executing Linear Temporal Logic (LTL) instructions with learned robot policies faces two practical challenges: demonstrations may cover individual behaviors without containing the temporal compositions requested at deployment, and semantic success predicates often provide little useful motor guidance through quantitati...

Moritz Zoellner, Anastasios Manganaris, A. H. Qureshi et al. · 1 citation

Preprint Sep 2026

Riemannian Splat Regression Models for Learning Time Fields on Arbitrary Riemannian Manifolds

Motion planning on arbitrary Riemannian manifolds is an important and difficult problem that frustrates typical planning methods for Euclidean spaces. In particular, motion planning methods that approximate optimal time-to-go functions with neural networks, e.g., Neural Time Fields (NTFields), cannot be directly applie...

Anastasios Manganaris, R. Banerjee, Gokul Prabhakaran et al. · 0 citations

#machine learning Preprint Aug 2026

hint$^2$: Hierarchical World Models for Inference-Time Temporal Logic Guidance

This paper introduces hint, a method for guiding short-horizon policies toward satisfying complex LTL specifications at inference time using hierarchical world models, and shows that hint$^2$ can handle complex instructions on a real UR5e manipulator.

Moritz Zoellner, Anastasios Manganaris, A. H. Qureshi et al. · 1 citation