Author

A. H. Qureshi

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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

#artificial intelligence Preprint Sep 2026

Missing Bridges: Composition-Aware Active Imitation Learning

This work introduces Adaptive Agents via Latent Topologies (AALT), which requests demonstrations that maximize expected gains in start-goal connectivity and shows that this objective is formally tied to information gain about task reachability.

M. Jacobson, A. H. Qureshi, Ye-Xiang Xue · 0 citations

Preprint Aug 2026

Coordinated Motion Planning for Multi-Arm Systems via Iterative LQ Games

This paper presents an iterative Linear Quadratic (LQ) game framework for multi-manipulator motion planning, where each manipulator is modeled as an independent agent optimizing its own objective while interacting with other agents based on shared global states and collision constraints.

Junyoung Kim, Han-Wen Ren, Lei Zhang et al. · 0 citations

Manip4Care+: Constraint-RRTConnect with Learning-Based Constraint Checking for Assistive Limb Manipulation

This paper presents Manip4Care+, which extends a modular simulation pipeline for grasping and repositioning human limbs using constrained MPPI and vector-field tracking with a projection-based Constraint-RRTConnect planner and a learned constraint checker for coupled robot–human states.

Kai Cheng, Yi Wu, A. H. Qureshi · 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