Hi there, this is Eugenio! I am a PhD candidate at the University of Freiburg under the supervision of Prof. Abhinav Valada and Wolfram Burgard. My research is focused on Robot Learning, in particular Imitation and Reinforcement Learning for mobile manipulation. In the past I contributed to different ambitious research and engineering projects and gathered firsthand experience in classical and optimal control systems and motion planning. In particular, I worked as Trajectory and Vehicle Dynamics Control Engineer at AMZ Driverless, ETH's Formula Student Driverless team, and interned at ANYbotics AG as Robotics Software Engineer in the autonomous navigation team. I was also for two years in a row Teaching Assistant at ETH's Institute for Dynamic Systems and Control under Prof. Emilio Frazzoli for the lectures Control Systems I & II.
Learning to solve complex manipulation tasks from visual observations is a dominant challenge for real-world robot learning. Deep reinforcement learning algorithms have recently demonstrated impressive results, although they still require an impractical amount of time-consuming trial-and-error iterations. In this work, we consider the promising alternative paradigm of interactive learning where a human teacher provides feedback to the policy during execution, as opposed to imitation learning where a pre-collected dataset of perfect demonstrations is used. Our proposed CEILing (Corrective and Evaluative Interactive Learning) framework combines both corrective and evaluative feedback from the teacher to train a stochastic policy in an asynchronous manner, and employs a dedicated mechanism to trade off human corrections with the robot’s own experience. We present results obtained with our framework in extensive simulation and real-world experiments that demonstrate that CEILing can effectively solve complex robot manipulation tasks directly from raw images in less than one hour of real-world training.
The goal of this thesis project was to tackle the autonomous racing problem using state of the art reinforcement learning algorithms to learn an optimal policy from scratch. In order to overcome model mismatch, also known as reality gap, I applied on the simulation the well known model randomization and a novel policy regularization strategy. The policy is then refined by training on the physical car and the achieved performance is comparable to that achieved previously by a model based controller.
ICRA 2021 paper
AMZ Racing is one of the world's leading Formula Student teams, and the team behind the world's fastest accelerating electric vehicle. Each year two new teams of students are formed: the Electric team sets out to design, manufacture, test and race the fastest electric race car, while the Driverless team transforms the car from the previous year in an autonomous machine. The AMZ Driverless Team needs to tackle different challenges in order to build a fast autonomous race car: perception, velocity estimation, SLAM, sensor synchronization, motion planning, vehicle control, continuous integration, data management, safety systems, computing hardware.
I was responsible for the control and motion planning algorithms: I used the Delaunay triangulation to discretize the search space, applied beam search to find candidate paths and a cost function to evaluate them. Our tireless work resulted in an autonomous race car capable of securing the overall first place at each competition we participated in.
JFR 2020 Paper
In this course project I implemented, tuned and tested different deep learning algorithms. The goal was to predict how a certain motion continues for several frames in the future, given many sequences of pre-recorded human motion data. I framed the problem as sequence-to-sequence learning and used a LSTM based encoder-decoder structure trained on multi-steps predictions. To enforce continuity between prediction steps, I adopted a residual architecture. The model was able to perform beyond the hard baseline.
REPORTIn this thesis project I designed, implemented and tested different control algorithms from scratch, with the goal of improving the energy management system of the hybrid electric power unit used in Formula 1. To take the non-smooth structure of the problem explicitly into account, I adapted the optimal control policy in real-time with simple PID controllers leveraging Pontryagin’s minimum principle. The performance of the presented approach was better compared to the performance of a previously developed optimality tracking MPC scheme.
CDC 2018 Paper
