20 citations · 52 across the 14 of their papers we have counts for
14 papers
CoCoNav: Conformal Control for Safe Robot Navigation in Crowds
Cheng Guo, Mingzhe Ni, Zheng Liang +5
Safe and efficient robot navigation in crowds requires anticipating pedestrian motion despite uncertain and potentially shifting prediction errors. Existing reactive methods can pr…
SKooP: Symmetric Koopman Predictions for Faster and More Generalizable Legged Robot Locomotion with Reinforcement Learning
Evelyn D'Elia, Weishu Zhan, Giulio Turrisi +5
Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency. In robotics, a recent line of work has emerged addressing this problem by encoding physics pr…
Physics-Informed Learning for Human Whole-Body Kinematics Prediction via Sparse IMUs
Cheng Guo, Giuseppe L'Erario, Giulio Romualdi +4
Accurate and physically feasible human motion prediction is crucial for safe and seamless human-robot collaboration. While recent advancements in human motion capture enable real-t…
Stabilizing Humanoid Robot Trajectory Generation via Physics-Informed Learning and Control-Informed Steering
Evelyn D'Elia, Paolo Maria Viceconte, Lorenzo Rapetti +5
Recent trends in humanoid robot control have successfully employed imitation learning to enable the learned generation of smooth, human-like trajectories from human data. While the…
Data-fused MPC with Guarantees: Application to Flying Humanoid Robots
Davide Gorbani, Mohamed Elobaid, Giuseppe L'Erario +2
This paper introduces a Data-Fused Model Predictive Control (DFMPC) framework that combines physics-based models with data-driven representations of unknown dynamics. Leveraging Wi…
Physics-Informed Neural Networks with Unscented Kalman Filter for Sensorless Joint Torque Estimation in Humanoid Robots
Ines Sorrentino, Giulio Romualdi, Lorenzo Moretti +2
This paper presents a novel framework for whole-body torque control of humanoid robots without joint torque sensors, designed for systems with electric motors and high-ratio harmon…
Learning to Evaluate Autonomous Behaviour in Human-Robot Interaction
Matteo Tiezzi, Tommaso Apicella, Carlos Cardenas-Perez +5
Evaluating and comparing the performance of autonomous Humanoid Robots is challenging, as success rate metrics are difficult to reproduce and fail to capture the complexity of robo…
Physics-Informed Learning for the Friction Modeling of High-Ratio Harmonic Drives
Ines Sorrentino, Giulio Romualdi, Fabio Bergonti +3
This paper presents a scalable method for friction identification in robots equipped with electric motors and high-ratio harmonic drives, utilizing Physics-Informed Neural Networks…
Automatic Gain Tuning for Humanoid Robots Walking Architectures Using Gradient-Free Optimization Techniques
Carlotta Sartore, Marco Rando, Giulio Romualdi +3
Developing sophisticated control architectures has endowed robots, particularly humanoid robots, with numerous capabilities. However, tuning these architectures remains a challengi…
Remote telepresence over large distances via robot avatars: case studies
Mohamed Elobaid, Stefano Dafarra, Ehsan Ranjbari +5
This paper discusses the necessary considerations and adjustments that allow a recently proposed avatar system architecture to be used with different robotic avatar morphologies (b…
Adaptive Non-linear Centroidal MPC with Stability Guarantees for Robust Locomotion of Legged Robots
Mohamed Elobaid, Giulio Turrisi, Lorenzo Rapetti +7
Nonlinear model predictive locomotion controllers based on the reduced centroidal dynamics are nowadays ubiquitous in legged robots. These schemes, even if they assume an inherent…
XBG: End-to-end Imitation Learning for Autonomous Behaviour in Human-Robot Interaction and Collaboration
Carlos Cardenas-Perez, Giulio Romualdi, Mohamed Elobaid +6
This paper presents XBG (eXteroceptive Behaviour Generation), a multimodal end-to-end Imitation Learning (IL) system for a whole-body autonomous humanoid robot used in real-world H…
UKF-Based Sensor Fusion for Joint-Torque Sensorless Humanoid Robots
Ines Sorrentino, Giulio Romualdi, Daniele Pucci
This paper proposes a novel sensor fusion based on Unscented Kalman Filtering for the online estimation of joint-torques of humanoid robots without joint-torque sensors. At the fea…
A Structured Prediction Approach for Robot Imitation Learning
Anqing Duan, Iason Batzianoulis, Raffaello Camoriano +3
We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observe…