20 citations · 52 across the 14 of their papers we have counts for
5 papers · 1 filter
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…