6 papers
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…
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…