3 citations · 4 across the 8 of their papers we have counts for
7 papers · 1 filter
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, Noémie Jaquier
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…
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,…
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…
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…
PUMA: Deep Metric Imitation Learning for Stable Motion Primitives
Rodrigo Pérez-Dattari, Cosimo Della Santina, Jens Kober
Imitation Learning (IL) is a powerful technique for intuitive robotic programming. However, ensuring the reliability of learned behaviors remains a challenge. In the context of rea…
Interactive Imitation Learning in Robotics: A Survey
Carlos Celemin, Rodrigo Pérez-Dattari, Eugenio Chisari +7
Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL) where human feedback is provided intermittently during robot execution allowing an online improvement of…