I pursued my PhD in Computer Science at Prof. Stefan Schaal’s robot lab at the Max Planck Institute for Intelligent Systems, in Tübingen (Germany) and the University of Southern California. I was advised by Prof. Sebastian Trimpe and co-advised by Prof. Philipp Hennig. During my PhD, I collaborated with Prof. Jeannette Bohg, Prof. Angela P. Schoellig, Prof. Andreas Krause.
In 2019, I was a visiting researcher at the Computational and Biological Learning Lab, at University of Cambridge, UK, working with Prof. José Miguel Hernández-Lobato in Bayesian optimization. I also was a PhD intern at Meta AI (formerly FAIR) in California, working with Prof. Roberto Calandra in model-based reinforcement learning.
I am broadly interested in preventing unsafe behavior in autonomous systems that navigate through real-world unstructured environments. Specifically, I study out-of-distribution (OoD) run-time monitors that trigger a backup policy when the perceived environment is beyond generalization.
I also explore data-efficiency in model-based reinforcement learning by informing the probabilistic dynamics model with existing expert knowledge (e.g., physics models, high-fidelity simulators). In particular, I study Gaussian process state-space models and Bayesian networks.
At a high level, I am highly passionate about scaling theoretically sound ideas to real systems. During my academic journey I acquired hands-on expertise with quadrupeds, hexapods, bipeds and robot manipulators that navigate and interact in the real world.
Contact: amarco [at] berkeley [dot] edu
PhD in Robotics and Machine Learning, 2020
Max Planck Institute for Intelligent Systems and University of Tübingen, Germany
MSc in Artificial Intelligence, 2015
Polytechnical University of Catalonia, Spain
I presented our current progress on “Out of Distribution Detection via Simulation-Informed Gaussian Process State Space Models” at Prof. Jeannette Bohg’s group, the Interactive Perception and Robot Learning Lab at Stanford.
Our paper “Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models” has been accepted to CDC, held at Marina Bay Sands, in Singapore.
I presented my work on “Online out-of-distribution detection via simulation-informed deep Gaussian process state-space models” at the DARPA Assured Neuro Symbolic Learning and Reasoning (ANSR) campus visit, held at UC Berkeley. Slides.
I am part of the best paper award committee for 5th Learning for Dynamics and Control Conference (L4DC), held at the University of Pennsylvania.
We’ve submitted our paper “Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models” to CDC, currently under review!
Our paper on Koopman-based Ljapunov functions has been accepted at the 61st IEEE Conference on Decision and Control (Mexico)!!
I presented a poster at the Safe Aviation Autonomy NASA ULI annual meeting, held at Stanford.
I am serving as part of the best paper award committee for 4th Learning for Dynamics and Control Conference (L4DC), held at Stanford.
I have moved in to Berkeley! I’ve joined the Hybrid Systems Lab as a postdoc, working with Prof. Claire Tomlin on model-based RL and kernel methods.
I have been awarded the Rafael del Pino Excellence Fellowship awarded to Spanish researchers with an outstanding academic path (1% acceptance rate).
I have been invited to give a talk (remotely) at the Learning for Dynamics and Control seminar at UC Berkeley, jointly organized by Prof. Koushil Sreenathat, Prof. Ben Recht and Prof. Francesco Borrelli’s groups.
I have defended my PhD at the University of Tübingen, Germany! My thesis entitled “Bayesian Optimization in Robot Learning: Automatic Controller Tuning and Sample-Efficient Methods” can be found here.
I have been invited to present (remotely) my PhD thesis at UC Berkeley, at Prof. Claire Tomlin’s group.
I have presented at Facebook Artificial Intelligence Research (FAIR) the work I did during my intership.
I have moved in to California for an internship at Facebook Artificial Intelligence Research (FAIR), working in model-based RL with Roberto Calandra.
I have presented a poster at the Div-f Conference, at the University of Cambridge, UK.
Our journal paper “Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study” has been published!
Up-to-date with my google scholar profile