9 papers
Edge Delayed Deep Deterministic Policy Gradient: efficient continuous control for edge scenarios
Alberto Sinigaglia, Niccolò Turcato, Ruggero Carli +1
Deep Reinforcement Learning is gaining increasing attention thanks to its capability to learn complex policies in high-dimensional settings. Recent advancements utilize a dual-netw…
Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots
Marco Calì, Alberto Sinigaglia, Niccolò Turcato +2
Deep Reinforcement Learning (RL) has emerged as a powerful method for addressing complex control problems, particularly those involving underactuated robotic systems. However, in s…
Towards Autonomous Reinforcement Learning for Real-World Robotic Manipulation with Large Language Models
Niccolò Turcato, Matteo Iovino, Aris Synodinos +3
Recent advancements in Large Language Models (LLMs) and Visual Language Models (VLMs) have significantly impacted robotics, enabling high-level semantic motion planning application…
Learning global control of underactuated systems with Model-Based Reinforcement Learning
Niccolò Turcato, Marco Calì, Alberto Dalla Libera +3
This short paper describes our proposed solution for the third edition of the "AI Olympics with RealAIGym" competition, held at ICRA 2025. We employed Monte-Carlo Probabilistic Inf…
Reinforcement Learning for Robust Athletic Intelligence: Lessons from the 2nd 'AI Olympics with RealAIGym' Competition
Felix Wiebe, Niccolò Turcato, Alberto Dalla Libera +17
In the field of robotics many different approaches ranging from classical planning over optimal control to reinforcement learning (RL) are developed and borrowed from other fields…
Data efficient Robotic Object Throwing with Model-Based Reinforcement Learning
Niccolò Turcato, Giulio Giacomuzzo, Matteo Terreran +3
Pick-and-place (PnP) operations, featuring object grasping and trajectory planning, are fundamental in industrial robotics applications. Despite many advancements in the field, PnP…