7 papers
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…
Riding the Shifting Potential: When Reactive Control Suffices for Multi-Goal Behavior
Vito Mengers, Oliver Brock
Reactive control is often considered insufficient for multi-objective tasks because conflicting objectives give rise to local minima. We argue this limitation is not inherent but a…
Probing Embodied LLMs: When Higher Observation Fidelity Hurts Problem Solving
Oussama Zenkri, Oliver Brock
Large Language Models are increasingly proposed as cognitive components for robotic systems, yet their opaque decision processes make it difficult to explain success or failure in…
From a Single Demonstration to a General Policy for Contact-Rich Manipulation
Xing Li, Oliver Brock
We present a Learning from Demonstration (LfD) framework that achieves one-shot generalization in multi-stage, contact-rich manipulation tasks. Central to our approach is the utili…
A Mechanistic Model for Collective Motion from Sensorimotor Regularities
Vito Mengers, Bao Duc Cao, Oliver Brock
Collective behavior in animals has long been modeled through self-propelled particle models, which reproduce striking group-level phenomena through abstract interaction forces. Yet…
No Plan, Yet Human: A Reactive Robotics Model Predicts Human Planning Failures on a Clinical Task
Michael Migacev, Vito Mengers, Antonia Köngeter +1
Understanding why some sequential planning problems are harder than others requires models that go beyond average performance. They should capture the specific pattern of which pro…