21 citations · 21 across the 4 of their papers we have counts for
5 papers
Embedding Physics Priors in Robot Learning: A Survey
Mattia Piccinini, Lucas Schulze, Alice Plebe +10
The rapid progress of artificial intelligence is reshaping robotics and accelerating the adoption of learning-based approaches. While purely data-driven methods have achieved remar…
Model-Structured Neural Networks to Control the Steering Dynamics of Autonomous Race Cars
Mattia Piccinini, Aniello Mungiello, Georg Jank +3
Autonomous racing has gained increasing attention in recent years, as a safe environment to accelerate the development of motion planning and control methods for autonomous driving…
CACTO-SL: Using Sobolev Learning to improve Continuous Actor-Critic with Trajectory Optimization
Elisa Alboni, Gianluigi Grandesso, Gastone Pietro Rosati Papini +2
Trajectory Optimization (TO) and Reinforcement Learning (RL) are powerful and complementary tools to solve optimal control problems. On the one hand, TO can efficiently compute loc…
CACTO: Continuous Actor-Critic with Trajectory Optimization -- Towards global optimality
Gianluigi Grandesso, Elisa Alboni, Gastone P. Rosati Papini +2
This paper presents a novel algorithm for the continuous control of dynamical systems that combines Trajectory Optimization (TO) and Reinforcement Learning (RL) in a single framewo…
Generating Reliable and Efficient Predictions of Human Motion: A Promising Encounter between Physics and Neural Networks
Alessandro Antonucci, Gastone Pietro Rosati Papini, Luigi Palopoli +1
Generating accurate and efficient predictions for the motion of the humans present in the scene is key to the development of effective motion planning algorithms for robots moving…