7 papers
Let the Dynamics Flow: Stable Flow Matching Dynamical Systems
Rodrigo Pérez-Dattari, Francisco Leiva, Andrea Testa +3
Flow matching has recently emerged as a powerful approach for imitation learning, enabling scalable, expressive, and multimodal motion policies. However, when modeling these polici…
Symmetries Here and There, Combined Everywhere: Cross-space Symmetry Compositions in Robotics
Loizos Hadjiloizou, Rodrigo Pérez-Dattari, Rodrigo Pérez-Dattari +2
Robots exhibit a rich variety of symmetries arising from their mechanical structure and the properties of their tasks. Although many robotics problems exhibit several symmetries si…
From Action Labels to Sets: Rethinking Action Supervision for Imitation Learning from Corrective Feedback
Zhaoting Li, Rodrigo Pérez-Dattari, Robert Babuska +2
Behavior cloning (BC) optimizes policies by treating human demonstrations as pointwise action labels. While effective with accurate action labels, this formulation is brittle in pr…
Learning to Move in Rhythm: Task-Conditioned Motion Policies with Orbital Stability Guarantees
Maximilian Stölzle, T. Konstantin Rusch, Zach J. Patterson +5
Learning from demonstration provides a sample-efficient approach to acquiring complex behaviors, enabling robots to move robustly, compliantly, and with fluidity. In this context,…
TamedPUMA: safe and stable imitation learning with geometric fabrics
Saray Bakker, Rodrigo Pérez-Dattari, Cosimo Della Santina +2
Using the language of dynamical systems, Imitation learning (IL) provides an intuitive and effective way of teaching stable task-space motions to robots with goal convergence. Yet,…
Scalable Task Planning via Large Language Models and Structured World Representations
Rodrigo Pérez-Dattari, Zhaoting Li, Robert Babuška +2
Planning methods struggle with computational intractability in solving task-level problems in large-scale environments. This work explores leveraging the commonsense knowledge enco…