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cs.RO2026

Prompting Robot Teams with Natural Language

Eduardo Sebastián, Nicolas Pfitzer, Ajay Shankar +1

This paper presents a framework to prompt multi-robot teams with high-level tasks using natural language expressions. Our objective is to use the reasoning capabilities of language…

cs.RO2026

World-Task Factorization for Robot Learning

Eduardo Sebastián, Adrian Pfisterer, Vito Mengers +2

Robot learning must produce policies that generalize to new combinations of constraints, teammates, and environments. To achieve this, we must structurally factor the policy, which…

cs.RO2026

Differentiable Environment-Trajectory Co-Optimization for Safe Multi-Agent Navigation

Zhan Gao, Gabriele Fadini, Stelian Coros +1

The environment plays a critical role in multi-agent navigation by imposing spatial constraints, rules, and limitations that agents must navigate around. Traditional approaches tre…

cs.RO2026

db-LaCAM: Fast and Scalable Multi-Robot Kinodynamic Motion Planning with Discontinuity-Bounded Search and Lightweight MAPF

Akmaral Moldagalieva, Keisuke Okumura, Amanda Prorok +1

State-of-the-art multi-robot kinodynamic motion planners struggle to handle more than a few robots due to high computational burden, which limits their scalability and results in s…

cs.RO2026

Wake Up to the Past: Using Memory to Model Fluid Wake Effects on Robots

Luca Vendruscolo, Eduardo Sebastián, Amanda Prorok +1

Autonomous aerial and aquatic robots that attain mobility by perturbing their medium, such as multicopters and torpedoes, produce wake effects that act as disturbances for adjacent…

cs.RO2026

From Vision to Decision: Neuromorphic Control for Autonomous Navigation and Tracking

Chuwei Wang, Eduardo Sebastián, Amanda Prorok +1

Robotic navigation has historically struggled to reconcile reactive, sensor-based control with the decisive capabilities of model-based planners. This duality becomes critical when…