Ph.D. Rodrigo Pérez Dattari

About

I am a researcher specializing in robotics, with a focus on machine learning and control theory. My work aims to create robots that are robust and adaptable to unstructured environments, ensuring safe operation while facilitating ease of deployment. My experience includes developing advanced algorithms that learn from demonstrations, plan tasks effectively, and exploit prior knowledge to enhance performance. I’m excited to join a dynamic team to drive innovation in AI and apply cutting-edge solutions to real-world challenges.

Skills

Machine Learning for robotics: imitation/reinforcement learning, human-robot interactions, human-in-the-loop, safety guarantees (stability, collision avoidance), learning on Riemannian manifolds, and task planning via large language models.

Control: forward/inverse kinematics, impedance/velocity control, Operational space control, inverse dynamics control, model predictive control, visuomotor control.

Programming: ROS, python, deep learning (PyTorch, Tensorflow), git, C++, LaTeX, bash.

Project management: leading and contributing to collaborative research projects, supervising several undergraduate and graduate students.

Research & communication: authored multiple research papers and presented at international conferences (e.g., ICRA, CoRL, and ISER).

Languages: Spanish (native) and English (full professional proficiency).

Experience

2023-2024

Post-Doctoral Researcher @ Delft University of Technology

• Robotic imitation learning through dynamical-system-based priors, including non-Riemannian geometries, multistability, and limit cycles
• Large-scale task planning via large language models and graph-based knowledge representations

2019-2023

Ph.D. in Robotics @ Delft University of Technology

• Thesis: Generalizable Robotic Imitation Learning: Interactive Learning and Inductive Bias
• Interactive learning for data-efficient and human-friendly policy shaping
• Behavior priors based on control theory —MPC and dynamical systems theory— for increased data efficiency, reliability, and safety
• Combining several research projects into two proof-of-concept demonstrations: a greenhouse setup and a poultry processing setup

2016-2018

Development Engineer @ Papinotas (now Lirmi )

• Software and hardware projects for a school management app.

Education

2012-2019

Electrical Engineering — B.Sc. and M.Sc. @ University of Chile

• Focus on machine learning, robotics, control, telecommunications, and digital systems.
• Master thesis: Interactive Learning with Corrective Feedback for Continuous-Action Policies Based on Deep Neural Networks

Selected Projects

Dynamical systems for motion primitive learning

• Dynamical-system learning for robot control from demonstrations.
• Emphasis on introducing strong inductive biases in machine learning models.
• PyTorch-based implementation.

Interactive Imitation Learning

• Collection of works exploring robot policy learning methods from online human feedback (human-in-the-loop).
• Multiple deep learning frameworks and a comprehensive survey of the field.

KUKA iiwa control framework

• ROS-integrated framework featuring robot controllers tailored for research and educational applications.
• Codebase used as the backbone of multiple research projects.

Invited Talks and Lectures

Invited Talks

2024

• Generalizable Robotic Imitation Learning — Talk given at several German research institutes following the DAAD AInet Fellowship award . @ Bosch Center for Artificial Intelligence, @ Karlsruhe Institute of Technology (KIT), @ TU Darmstadt

Invited Lecturer

2024

• Course RO47019 Intelligent Control Systems @ Delft University of Technology — Lecture: Dynamical Systems for Imitation Learning

Interests

• Music (guitar, singing), hiking, chess, football

Publications

Journals

1. R. Pérez-Dattari, Z. Li, R. Babuška, J. Kober and C. Della Santina. “Leveraging LLMs, Graphs and Object Hierarchies for Task Planning in Large-Scale Environments”. (under review), 2024
2. R. Pérez-Dattari, C. Della Santina, and J. Kober. “PUMA: Deep metric imitation learning for stable motion primitives”. Advanced Intelligent Systems, 2024
3. R.Pérez-Dattari and J. Kober. “Stable motion primitives via imitation and contrastive learning”. IEEE Transactions on Robotics (T-RO), 2023
4. C. Celemin, R. Pérez-Dattari, E. Chisari, G. Franzese, L.de Souza Rosa, R. Prakash, Z. Ajanović, M. Ferraz, A. Valada, and J. Kober. “Interactive imitation learning in robotics: A survey”. Foundations and Trends® in Robotics, 2022
5. R. Pérez-Dattari, B. Brito, O. de Groot, J. Kober, and J. Alonso-Mora. “Visually-guided motion planning for autonomous driving from interactive demonstrations”. Engineering Applications of Artificial Intelligence, 2022
6. R. Pérez-Dattari, C. Celemin, G.Franzese, J. Ruiz-del Solar, and J. Kober. “Interactive learning of temporal features for control”. IEEE Robotics & Automation Magazine (RAM), 2020

Conferences

1. E.Drijver, R.Pérez-Dattari, J. Kober, C. Della Santina, and Z. Ajanović. “Robotic packaging optimization with reinforcement learning”. IEEE International Conference on Automation Science and Engineering (CASE), 2023
2. P. Valletta, R. Pérez-Dattari, and Jens Kober. “Imitation learning with inconsistent demonstrations through uncertainty-based data manipulation”. IEEE International Conference on Robotics and Automation (ICRA), 2021
3. R. Pérez-Dattari, C. Celemin, J. Ruiz-del Solar, and J. Kober. “Continuous control for high-dimensional state spaces: An interactive learning approach”. IEEE International Conference on Robotics and Automation (ICRA), 2019
4. R. Pérez-Dattari, C. Celemin, J. Ruiz-del Solar, and J. Kober. “Interactive learning with corrective feedback for policies based on deep neural networks”. International Symposium on Experimental Robotics (ISER), Springer, 2018
5. C. Celemin, R. Pérez-Dattari, J. Ruiz-del Solar, and M. Veloso. “Interactive machine learning applied to dribble a ball in soccer with biped robots”. RoboCup 2017: Robot World Cup XXI 11, Springer, 2018