9 citations · 13 across the 17 of their papers we have counts for
20 papers
DROM: A Language-Guided Diffusion Framework for Multi-Skill Robotic Manipulation
Vincenzo Pomponi, Rocco Felici, Paolo Franceschi +5
Learning robust manipulation policies for diverse, long-horizon tasks from limited demonstrations remains a fundamental challenge in robotics. We present DROM, a language-guided di…
Diffusion Sequence Models for Generative In-Context Meta-Learning of Robot Dynamics
Angelo Moroncelli, Matteo Rufolo, Gunes Cagin Aydin +2
Accurate modeling of robot dynamics is essential for model-based control, yet remains challenging under distributional shifts and real-time constraints. In this work, we formulate…
Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents
Angelo Moroncelli, Roberto Zanetti, Marco Maccarini +1
Reinforcement learning (RL) enables high-frequency, closed-loop control for robotic manipulation, but scaling to long-horizon tasks with sparse or imperfect rewards remains difficu…
From Vision to Assistance: Gaze and Vision-Enabled Adaptive Control for a Back-Support Exoskeleton
Alessandro Leanza, Paolo Franceschi, Blerina Spahiu +1
Back-support exoskeletons have been proposed to mitigate spinal loading in industrial handling, yet their effectiveness critically depends on timely and context-aware assistance. M…
DynaMimicGen: A Data Generation Framework for Robot Learning of Dynamic Tasks
Vincenzo Pomponi, Paolo Franceschi, Stefano Baraldo +4
Learning robust manipulation policies typically requires large and diverse datasets, the collection of which is time-consuming, labor-intensive, and often impractical for dynamic e…
Augmenting Neural Networks-Based Model Approximators in Robotic Force-Tracking Tasks
Kevin Saad, Vincenzo Petrone, Enrico Ferrentino +3
As robotics gains popularity, interaction control becomes crucial for ensuring force tracking in manipulator-based tasks. Typically, traditional interaction controllers either requ…
Robotic Skill Diversification via Active Mutation of Reward Functions in Reinforcement Learning During a Liquid Pouring Task
Jannick van Buuren, Roberto Giglio, Loris Roveda +1
This paper explores how deliberate mutations of reward function in reinforcement learning can produce diversified skill variations in robotic manipulation tasks, examined with a li…
ConceptBot: Enhancing Robot's Autonomy through Task Decomposition with Large Language Models and Knowledge Graph
Alessandro Leanza, Angelo Moroncelli, Giuseppe Vizzari +3
ConceptBot is a modular robotic planning framework that combines Large Language Models and Knowledge Graphs to generate feasible and risk-aware plans despite ambiguities in natural…
GEAR: Gaze-Enabled Human-Robot Collaborative Assembly
Asad Ali Shahid, Angelo Moroncelli, Drazen Brscic +2
Recent progress in robot autonomy and safety has significantly improved human-robot interactions, enabling robots to work alongside humans on various tasks. However, complex assemb…
Robot Drummer: Learning Rhythmic Skills for Humanoid Drumming
Asad Ali Shahid, Francesco Braghin, Loris Roveda
Humanoid robots have seen remarkable advances in dexterity, balance, and locomotion, yet their role in expressive domains such as music performance remains largely unexplored. Musi…
The Duality of Generative AI and Reinforcement Learning in Robotics: A Review
Angelo Moroncelli, Vishal Soni, Marco Forgione +3
Recently, generative AI and reinforcement learning (RL) have been redefining what is possible for AI agents that take information flows as input and produce intelligent behavior. A…
RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling
Manuel Bianchi Bazzi, Asad Ali Shahid, Christopher Agia +6
The landscape of Deep Learning has experienced a major shift with the pervasive adoption of Transformer-based architectures, particularly in Natural Language Processing (NLP). Nove…
Benchmarking Population-Based Reinforcement Learning across Robotic Tasks with GPU-Accelerated Simulation
Asad Ali Shahid, Yashraj Narang, Vincenzo Petrone +5
In recent years, deep reinforcement learning (RL) has shown its effectiveness in solving complex continuous control tasks. However, this comes at the cost of an enormous amount of…
Combining Sampling- and Gradient-based Planning for Contact-rich Manipulation
Filippo Rozzi, Loris Roveda, Kevin Haninger
Planning over discontinuous dynamics is needed for robotics tasks like contact-rich manipulation, which presents challenges in the numerical stability and speed of planning methods…
Imitation Learning-based Visual Servoing for Tracking Moving Objects
Rocco Felici, Matteo Saveriano, Loris Roveda +1
In everyday life collaboration tasks between human operators and robots, the former necessitate simple ways for programming new skills, the latter have to show adaptive capabilitie…
Predicting human motion intention for pHRI assistive control
Paolo Franceschi, Fabio Bertini, Francesco Braghin +3
This work addresses human intention identification during physical Human-Robot Interaction (pHRI) tasks to include this information in an assistive controller. To this purpose, hum…
Teaching contact-rich tasks from visual demonstrations by constraint extraction
Christian Hegeler, Filippo Rozzi, Loris Roveda +1
Contact-rich manipulation involves kinematic constraints on the task motion, typically with discrete transitions between these constraints during the task. Allowing the robot to de…
Differentiable Compliant Contact Primitives for Estimation and Model Predictive Control
Kevin Haninger, Kangwagye Samuel, Filippo Rozzi +2
Control techniques like MPC can realize contact-rich manipulation which exploits dynamic information, maintaining friction limits and safety constraints. However, contact geometry…
Experience in Engineering Complex Systems: Active Preference Learning with Multiple Outcomes and Certainty Levels
Le Anh Dao, Loris Roveda, Marco Maccarini +8
Black-box optimization refers to the optimization problem whose objective function and/or constraint sets are either unknown, inaccessible, or non-existent. In many applications, e…
Environment-based Assistance Modulation for a Hip Exosuit via Computer Vision
Enrica Tricomi, Mirko Mossini, Francesco Missiroli +4
Just like in humans vision plays a fundamental role in guiding adaptive locomotion, when designing the control strategy for a walking assistive technology, Computer Vision may brin…