12 papers · 1 filter
Hierarchical Topology-Aware Planning and Control of Underwater Vehicle-Manipulator Systems in Confined Environments
Mohamed Abdelwahab, Ruggero Carli, Damiano Varagnolo +1
This paper addresses autonomous intervention with an underwater vehicle--manipulator system (UVMS) in confined, cluttered, and partially known environments, where poor maneuverabil…
Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision
Alessandro Adami, Tommaso Tubaldo, Marco Todescato +2
Vision-Language Models (VLMs) have recently demonstrated strong capabilities in mapping multimodal observations to robot behaviors. However, most current approaches rely on end-to-…
Real2Sim via Active Perception with Behavior Trees Automatically Generated by VLMs
Alessandro Adami, Sebastian Zudaire, Ruggero Carli +1
Constructing physically accurate simulation environments (Real2Sim) traditionally relies on manual system identification or rigid, exhaustive exploration routines. These task-agnos…
Learning Stack-of-Tasks Management for Redundant Robots
Alessandro Adami, Aris Synodinos, Matteo Iovino +2
This paper presents a novel framework for automatically learning complete Stack-of-Tasks (SoT) controllers for redundant robotic systems, including task priorities, activation logi…
A Robust Controller based on Gaussian Processes for Robotic Manipulators with Unknown Uncertainty
Giulio Giacomuzzo, Mohamed Abdelwahab, Marco Calì +2
In this paper, we propose a novel learning-based robust feedback linearization strategy to ensure precise trajectory tracking for an important family of Lagrangian systems. We assu…
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…
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…
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…
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…
AI Olympics challenge with Evolutionary Soft Actor Critic
Marco Calì, Alberto Sinigaglia, Niccolò Turcato +2
In the following report, we describe the solution we propose for the AI Olympics competition held at IROS 2024. Our solution is based on a Model-free Deep Reinforcement Learning ap…
Learning control of underactuated double pendulum with Model-Based Reinforcement Learning
Niccolò Turcato, Alberto Dalla Libera, Giulio Giacomuzzo +2
This report describes our proposed solution for the second AI Olympics competition held at IROS 2024. Our solution is based on a recent Model-Based Reinforcement Learning algorithm…
Adaptive Robust Controller for handling Unknown Uncertainty of Robotic Manipulators
Mohamed Abdelwahab, Giulio Giacomuzzo, Alberto Dalla Libera +1
The ability to achieve precise and smooth trajectory tracking is crucial for ensuring the successful execution of various tasks involving robotic manipulators. State-of-the-art tec…