Pietro Falco — accepted AI-conference papers

2025

PACE: Proactive Assistance in Human-Robot Collaboration Through Action-Completion Estimation

Davide De Lazzari, Matteo Terreran, Giulio Giacomuzzo, Siddarth Jain, Pietro Falco, Ruggero Carli +1

ICRA 2025

This paper introduces the Proactive Assistance through action-Completion Estimation (PACE) framework, designed to enhance human-robot collaboration through real-time monitoring of human progress. PACE incorporates a novel method that combines Dynamic Time Warping (DTW) with correlation analysis to t

Cited by 3 Source ↗Scholar ↗

2025

Towards Autonomous Reinforcement Learning for Real-World Robotic Manipulation With Large Language Models

Niccolò Turcato, Matteo Iovino, Aris I. Synodinos, Alberto Dalla Libera, Ruggero Carli, Pietro Falco

RA-L 2025

Recent advancements in Large Language Models (LLMs) and Visual Language Models (VLMs) have significantly impacted robotics, enabling high-level semantic motion planning applications. Reinforcement Learning (RL), a complementary paradigm, enables agents to autonomously optimize complex behaviors thro

Cited by 3 Source ↗Scholar ↗

2023

On the programming effort required to generate Behavior Trees and Finite State Machines for robotic applications

Matteo Iovino, Julian Förster, Pietro Falco, Jen Jen Chung, Roland Siegwart, Christian Smith

ICRA 2023poster

In this paper we provide a practical demonstration of how the modularity in a Behavior Tree (BT) decreases the effort in programming a robot task when compared to a Finite State Machine (FSM). In recent years the way to represent a task plan to control an autonomous agent has been shifting from the…

Cited by 36 Source ↗Scholar ↗

2021

Learning Behavior Trees with Genetic Programming in Unpredictable Environments

Matteo Iovino, Jonathan Styrud, Pietro Falco, Christian Smith

ICRA 2021poster

Modern industrial applications require robots to operate in unpredictable environments, and programs to be created with a minimal effort, to accommodate frequent changes to the task. Here, we show that genetic programming can be effectively used to learn the structure of a behavior tree (BT) to solv…

Cited by 61 Source ↗Scholar ↗

2021

Learning Deep Energy Shaping Policies for Stability-Guaranteed Manipulation

Shahbaz Abdul Khader, Hang Yin, Pietro Falco, Danica Kragic

RA-L 2021

Deep reinforcement learning (DRL) has been successfully used to solve various robotic manipulation tasks. However, most of the existing works do not address the issue of control stability. This is in sharp contrast to the control theory community where the well-established norm is to prove stability

Cited by 16 Source ↗Scholar ↗

2021

Learning Stable Normalizing-Flow Control for Robotic Manipulation

Shahbaz Abdul Khader, Hang Yin, Pietro Falco, Danica Kragic

ICRA 2021poster

Reinforcement Learning (RL) of robotic manipulation skills, despite its impressive successes, stands to benefit from incorporating domain knowledge from control theory. One of the most important properties that is of interest is control stability. Ideally, one would like to achieve stability guarant…

Cited by 19 Source ↗Scholar ↗

2021

Stability-Guaranteed Reinforcement Learning for Contact-Rich Manipulation

Shahbaz Abdul Khader, Hang Yin, Pietro Falco, Danica Kragic

RA-L 2021

Reinforcement learning (RL) has had its fair share of success in contact-rich manipulation tasks but it still lags behind in benefiting from advances in robot control theory such as impedance control and stability guarantees. Recently, the concept of variable impedance control (VIC) was adopted into

Cited by 51 Source ↗Scholar ↗

2020

Data-Efficient Model Learning and Prediction for Contact-Rich Manipulation Tasks

Shahbaz Abdul Khader, Hang Yin, Pietro Falco, Danica Kragic

RA-L 2020

In this letter, we investigate learning forward dynamics models and multi-step prediction of state variables (long-term prediction) for contact-rich manipulation. The problems are formulated in the context of model-based reinforcement learning (MBRL). We focus on two aspects-discontinuous dynamics a

Cited by 18 Source ↗Scholar ↗

2019

Variational Object-Aware 3-D Hand Pose From a Single RGB Image

Yafei Gao, Yida Wang, Pietro Falco, Nassir Navab, Federico Tombari

RA-L 2019

We propose an approach to estimate the 3D pose of a human hand while grasping objects from a single RGB image. Our approach is based on a probabilistic model implemented with deep architectures, which is used for regressing, respectively, the 2D hand joints heat maps and the 3D hand joints coordinat

Cited by 12 Source ↗Scholar ↗

2018

A Brief Survey on the Role of Dimensionality Reduction in Manipulation Learning and Control

Fanny Ficuciello, Pietro Falco, Sylvain Calinon

RA-L 2018

Bio-inspired designs are motivated by efficiency, adaptability, and robustness of biological systems' dynamic behaviors in complex environment. Despite progress in design, the lack of sensorimotor and learning capabilities is the main drawback of humanlike manipulation systems. Dimensionality reduct

Cited by 10 Source ↗Scholar ↗

2018

On Policy Learning Robust to Irreversible Events: An Application to Robotic In-Hand Manipulation

Pietro Falco, Abdallah Attawia, Matteo Saveriano, Dongheui Lee

RA-L 2018

In this letter, we present an approach for learning in-hand manipulation skills with a low-cost, underactuated prosthetic hand in the presence of irreversible events. Our approach combines reinforcement learning based on visual perception with low-level reactive control based on tactile perception,

Cited by 31 Source ↗Scholar ↗

2017

A Human Action Descriptor Based on Motion Coordination

Pietro Falco, Matteo Saveriano, Eka Gibran Hasany, Nicholas H. Kirk, Dongheui Lee

RA-L 2017

In this paper, we present a descriptor for human whole-body actions based on motion coordination. We exploit the principle, well known in neuromechanics, that humans move their joints in a coordinated fashion. Our coordination-based descriptor (CODE) is computed by two main steps. The first step is

Cited by 9 Source ↗Scholar ↗

2017

Cross-modal visuo-tactile object recognition using robotic active exploration

Pietro Falco, Shuang Lu, Andrea Cirillo, Ciro Natale, Salvatore Pirozzi, Dongheui Lee

ICRA 2017poster

In this work, we propose a framework to deal with cross-modal visuo-tactile object recognition. By cross-modal visuo-tactile object recognition, we mean that the object recognition algorithm is trained only with visual data and is able to recognize objects leveraging only tactile perception. The pro…

Cited by 76 Source ↗Scholar ↗

2017

Data-efficient control policy search using residual dynamics learning

Matteo Saveriano, Yuchao Yin, Pietro Falco, Dongheui Lee

IROS 2017poster

In this work, we propose a model-based and data efficient approach for reinforcement learning. The main idea of our algorithm is to combine simulated and real rollouts to efficiently find an optimal control policy. While performing rollouts on the robot, we exploit sensory data to learn a probabilis…

Cited by 66 Source ↗Scholar ↗

2016

Encoding human actions with a frequency domain approach

Dharmil Shah, Pietro Falco, Matteo Saveriano, Dongheui Lee

IROS 2016poster

In this work, we propose a Frequency-based Action Descriptor (FADE) to represent human actions. In robotics, with the development of Programming by Demonstration (PbD) methods, representing and recognizing large sets of actions has become crucial to build autonomous systems that learn from humans. T…

Cited by 8 Source ↗Scholar ↗

2015

Integrated force/tactile sensing: The enabling technology for slipping detection and avoidance

Giuseppe De Maria, Pietro Falco, Ciro Natale, Salvatore Pirozzi

ICRA 2015poster

This paper proposes an experimental study of slipping avoidance algorithms based on force/tactile perception data. The claim is that contact force measurements alone or tactile data alone are not sufficient for an effective slipping avoidance strategy in real world conditions. Integrated force/tacti…

Cited by 50 Source ↗Scholar ↗