Francesco Nori — accepted AI-conference papers

2025

DemoStart: Demonstration-Led Auto-Curriculum Applied to Sim-to-Real with Multi-Fingered Robots

Maria Bauzá, Jose Enriaue Chen, Valentin Dalibard, Nimrod Gileadi, Roland Hafner, Murilo F. Martins +10

ICRA 2025

We present DemoStart, a novel auto-curriculum reinforcement learning method capable of learning complex manipulation behaviors on an arm equipped with a three- fingered robotic hand, from only a sparse reward and a handful of demonstrations in simulation. Learning from simulation drastically reduces

Cited by 12 Source ↗Scholar ↗

2024

Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning

Dhruva Tirumala, Markus Wulfmeier, Ben Moran, Sandy Huang, Jan Humplik, Guy Lever +10

CoRL 2024poster

We apply multi-agent deep reinforcement learning (RL) to train end-to-end robot soccer policies with fully onboard computation and sensing via egocentric RGB vision. This setting reflects many challenges of real-world robotics, including active perception, agile full-body control, and long-horizon p…

Cited by 13 Source ↗Scholar ↗

2024

Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots

Thomas Lampe, Abbas Abdolmaleki, Sarah Bechtle, Sandy H. Huang, Jost Tobias Springenberg, Michael Bloesch +9

ICRA 2024poster

Reinforcement learning solely from an agent’s self-generated data is often believed to be infeasible for learning on real robots, due to the amount of data needed. However, if done right, agents learning from real data can be surprisingly efficient through re-using previously collected sub-optimal d…

Cited by 6 Source ↗Scholar ↗

2024

The Design of the Barkour Benchmark for Robot Agility

Wenhao Yu, Ken Caluwaerts, Atil Iscen, J. Chase Kew, Tingnan Zhang, Daniel Freeman +33

IROS 2024poster

In this paper, we describe the design of the Barkour benchmark for measuring robot agility in navigating complex environments. Despite the growing interest in developing agile robot locomotion skills, the field lacks systematic benchmarks to measure the performance of robotic control systems and har…

Cited by 1 Source ↗Scholar ↗

2023

NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields

Arunkumar Byravan, Jan Humplik, Leonard Hasenclever, Arthur Brussee, Francesco Nori, Tuomas Haarnoja +5

ICRA 2023poster

We present a system for applying sim2real approaches to “in the wild” scenes with realistic visuals, and to policies which rely on active perception using RGB cameras. Given a short video of a static scene collected using a generic phone, we learn the scene's contact geometry and a function for nove…

Cited by 57 Source ↗Scholar ↗

2021

Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes

Alex X. Lee, Coline Manon Devin, Yuxiang Zhou, Thomas Lampe, Konstantinos Bousmalis, Jost Tobias Springenberg +15

CoRL 2021poster

We study the problem of robotic stacking with objects of complex geometry. We propose a challenging and diverse set of such objects that was carefully designed to require strategies beyond a simple “pick-and-place” solution. Our method is a reinforcement learning (RL) approach combined with vision-b…

Cited by 118 Source ↗code ↗Scholar ↗

2020

Learning Dexterous Manipulation from Suboptimal Experts

Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Dan Zheng, Alexandre Galashov, Nicolas Heess +1

CoRL 2020

Learning dexterous manipulation in high-dimensional state-action spaces is an important open challenge with exploration presenting a major bottleneck. Although in many cases the learning process could be guided by demonstrations or other suboptimal experts, current RL algorithms for continuous actio

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2020

Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation

Rae Jeong, Yusuf Aytar, David Khosid, Yuxiang Zhou, Jackie Kay, Thomas Lampe +2

ICRA 2020poster

Collecting and automatically obtaining reward signals from real robotic visual data for the purposes of training reinforcement learning algorithms can be quite challenging and time-consuming. Methods for utilizing unlabeled data can have a huge potential to further accelerate robotic learning. We co…

Cited by 78 Source ↗Scholar ↗

2019

Model Based In Situ Calibration with Temperature compensation of 6 axis Force Torque Sensors

Francisco Javier Andrade Chavez, Gabriele Nava, Silvio Traversaro, Francesco Nori, Daniele Pucci

ICRA 2019poster

It is well known that sensors using strain gauges have a potential dependency on temperature. This creates temperature drift in the measurements of six axis force torque sensors (F/T). The temperature drift can be considerable if an experiment is long or the environmental conditions are different fr…

Cited by 17 Source ↗Scholar ↗

2019

Simultaneously Learning Vision and Feature-Based Control Policies for Real-World Ball-In-A-Cup

Devin Schwab, Jost Tobias Springenberg, Murilo Fernandes Martins, Michael Neunert, Thomas Lampe, Abbas Abdolmaleki +4

RSS 2019poster

We present a method for fast training of vision based control policies on real robots. The key idea behind our method is to perform multi-task Reinforcement Learning with auxiliary tasks that differ not only in the reward to be optimized but also in the state-space in which they operate. In particul…

Cited by 31 Source ↗PDF ↗Scholar ↗

2018

A Control Architecture with Online Predictive Planning for Position and Torque Controlled Walking of Humanoid Robots

Stefano Dafarra, Gabriele Nava, Marie Charbonneau, Nuno Guedelha, Francisco Andrade, Silvio Traversaro +5

IROS 2018poster

A common approach to the generation of walking patterns for humanoid robots consists in adopting a layered control architecture. This paper proposes an architecture composed of three nested control loops. The outer loop exploits a robot kinematic model to plan the footstep positions. In the mid laye…

Cited by 20 Source ↗Scholar ↗

2018

A Plenum-Based Calibration Device for Tactile Sensor Arrays

Joan Kangro, Anand Vazhapilli Sureshbabu, Silvio Traversaro, Daniele Pucci, Francesco Nori

RA-L 2018

In modern robotic applications, tactile sensor arrays (i.e., artificial skins) are an emergent solution to determine the locations of contacts between a robot and an external agent. Localizing the point of contact is useful but determining the force applied on the skin provides many additional possi

Cited by 6 Source ↗Scholar ↗

2018

Contact Force and Joint Torque Estimation Using Skin

Francisco Javier Andrade Chavez, Joan Kangro, Silvio Traversaro, Francesco Nori, Daniele Pucci

RA-L 2018

In this letter, we present algorithms to estimate contact locations, external forces, and joint torques using skin, i.e., distributed tactile sensors, kinematic sensors, and a single Inertial Measurement Unit (IMU) without the need for force-torque sensors. Distributed tactile sensors are an array o

Cited by 11 Source ↗Scholar ↗

2018

Momentum Control of an Underactuated Flying Humanoid Robot

Daniele Pucci, Silvio Traversaro, Francesco Nori

RA-L 2018

The letter takes the first step toward the development of a control framework for underactuated flying humanoid robots. These robots may thus have the capacities of flight, contact locomotion, and manipulation, and benefit from technologies and methods developed for Whole-Body Control and Aerial Man

Cited by 40 Source ↗Scholar ↗

2018

The CoDyCo Project Achievements and Beyond: Toward Human Aware Whole-Body Controllers for Physical Human Robot Interaction

Francesco Romano, Gabriele Nava, Morteza Azad, Jernej Camernik, Stefano Dafarra, Oriane Dermy +12

RA-L 2018

The success of robots in real-world environments is largely dependent on their ability to interact with both humans and said environment. The FP7 EU project CoDyCo focused on the latter of these two challenges by exploiting both rigid and compliant contacts dynamics in the robot control problem. Reg

Cited by 32 Source ↗Scholar ↗

2017

Control of humanoid robot motions with impacts: Numerical experiments with reference spreading control

Mark Rijnen, Eric de Mooij, Silvio Traversaro, Francesco Nori, Nathan van de Wouw, Alessandro Saccon +1

ICRA 2017poster

This work explores the stabilization of desired dynamic motion tasks involving hard impacts at non-negligible speed for humanoid robots. To this end, a so-called reference spreading hybrid control law is designed showing promising results in simulation. The simulations are performed employing a dyna…

Cited by 41 Source ↗Scholar ↗

2017

Momentum control of humanoid robots with series elastic actuators

Gabriele Nava, Daniele Pucci, Francesco Nori

IROS 2017poster

Humanoid robots may require a degree of compliance at joint level for improving efficiency, shock tolerance, and safe interaction with humans. The presence of joint elasticity, however, complexifies the control design of humanoid robots. This paper proposes a control framework to extend momentum bas…

Cited by 8 Source ↗Scholar ↗

2017

On Centroidal Dynamics and Integrability of Average Angular Velocity

Alessandro Saccon, Silvio Traversaro, Francesco Nori, Henk Nijmeijer

RA-L 2017

In the literature on robotics and multibody dynamics, the concept of average angular velocity has received considerable attention in recent years. We address the question of whether the average angular velocity defines an orientation frame that depends only on the current robot configuration and pro

Cited by 25 Source ↗Scholar ↗

2017

Skin normal force calibration using vacuum bags

Joan Kangro, Silvio Traversaro, Daniele Pucci, Francesco Nori

ICRA 2017poster

This paper presents a proof of concept to calibrate iCub's skin using vacuum bags. The main idea of the method consists of inserting the skin, made of distributed capacitive sensors, in a vacuum bag and then decreasing the pressure in the bag to create a uniform pressure distribution on the skin sur…

Cited by 9 Source ↗Scholar ↗

2016

Identification of fully physical consistent inertial parameters using optimization on manifolds

Silvio Traversaro, Stanislas Brossette, Adrien Escande, Francesco Nori

IROS 2016poster

This paper presents a new condition, the fully physical consistency for a set of inertial parameters to determine if they can be generated by a physical rigid body. The proposed condition ensure both the positive definiteness and the triangular inequality of 3D inertia matrices as opposed to existin…

Cited by 83 Source ↗Scholar ↗

2016

Incremental semiparametric inverse dynamics learning

Raffaello Camoriano, Silvio Traversaro, Lorenzo Rosasco, Giorgio Metta, Francesco Nori

ICRA 2016

This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior informatio

Cited by 51 Source ↗Scholar ↗

2016

Stability analysis and design of momentum-based controllers for humanoid robots

Gabriele Nava, Francesco Romano, Francesco Nori, Daniele Pucci

IROS 2016poster

Envisioned applications for humanoid robots call for the design of balancing and walking controllers. While promising results have been recently achieved, robust and reliable controllers are still a challenge for the control community dealing with humanoid robotics. Momentum-based strategies have pr…

Cited by 81 Source ↗Scholar ↗

2015

In situ calibration of six-axis force-torque sensors using accelerometer measurements

Silvio Traversaro, Daniele Pucci, Francesco Nori

ICRA 2015poster

This paper proposes techniques to calibrate six-axis force-torque sensors that can be performed in situ, i.e., without removing the sensor from the hosting system. We assume that the force-torque sensor is attached to a rigid body equipped with an accelerometer. Then, the proposed calibration techni…

Cited by 35 Source ↗Scholar ↗

2015

Inertial parameters identification and joint torques estimation with proximal force/torque sensing

Silvio Traversaro, Andrea Del Prete, Serena Ivaldi, Francesco Nori

ICRA 2015poster

Classically robot force control passes through joint torques measurement or estimation. Within this context, classical torque sensing technologies rely on current sensing on motor windings and on torsion sensing on motor shaft. An alternative approach was recently proposed in [1] and combines whole-…

Cited by 20 Source ↗Scholar ↗

2015

Multimodal sensor fusion for foot state estimation in bipedal robots using the Extended Kalman Filter

Jorhabib Eljaik, Naveen Kuppuswamy, Francesco Nori

IROS 2015poster

Towards enhancing the dynamic locomotion and manipulation abilities of bipedal robots in real-world scenarios, a key problem lies in the accurate estimation of the dynamic state of the feet of the robot. In this paper, an approach is presented for estimating the dynamic pose and the internal (body)…

Cited by 12 Source ↗Scholar ↗

2015

Prioritized optimal control: A hierarchical differential dynamic programming approach

Francesco Romano, Andrea Del Prete, Nicolas Mansard, Francesco Nori

ICRA 2015poster

This paper deals with the generation of motion for complex dynamical systems (such as humanoid robots) to achieve several concurrent objectives. Hierarchy of tasks and optimal control are two frameworks commonly used to this aim. The first one specifies control objectives as a number of quadratic fu…

Cited by 19 Source ↗Scholar ↗

2015

Simultaneous state and dynamics estimation in articulated structures

Francesco Nori, Naveen Kuppuswamy, Silvio Traversaro

IROS 2015poster

Given an articulated rigid body, we define the problem of estimating its dynamics as the problem of computing all the forces and accelerations acting on the bodies which constitute the articulated system. Similarly, we define the state estimation problem as the problem of computing the system positi…

Cited by 17 Source ↗Scholar ↗