1 citations · 1 across the 5 of their papers we have counts for
10 papers
Efficient Real-World Online Reinforcement Learning for Robot Manipulation via Centralized Training and Critic Decomposition
Changhao Li, Yifang Zhang, Heng Zhang +6
Real-world online reinforcement learning (RL) provides a promising approach for training robotic manipulation policies directly in the physical world, avoiding the sim-to-real gap…
Learning Fault-Tolerant Locomotion with Adaptive Gait Timing
Giovanbattista Gravina, Luca Rossini, Carlo Rizzardo +2
Hardware failures require legged robots to rapidly reorganize coordination and gait timing to maintain stability and mobility. This is particularly challenging for larger quadruped…
KYON: Semi-Modular Wheel-Legged Quadruped With Agile Bimanual Capability
Luca Rossini, Arturo Laurenzi, Francesco Ruscelli +8
This paper presents KYON, a hybrid wheel-legged quadruped robot equipped with a bimanual upper body for loco-manipulation tasks. The platform features a semi-modular design with a…
RL-Augmented MPC for Non-Gaited Legged and Hybrid Locomotion
Andrea Patrizi, Carlo Rizzardo, Arturo Laurenzi +3
We propose a contact-explicit hierarchical architecture coupling Reinforcement Learning (RL) and Model Predictive Control (MPC), where a high-level RL agent provides gait and navig…
A Task-Driven, Planner-in-the-Loop Computational Design Framework for Modular Manipulators
Maolin Lei, Edoardo Romiti, Arturo Laurenzi +5
Modular manipulators composed of pre-manufactured and interchangeable modules offer high adaptability across diverse tasks. However, their deployment requires generating feasible m…
Unified Hierarchical MPC in Task Executing for Modular Manipulators across Diverse Morphologies
Maolin Lei, Edoardo Romiti, Arturo Laurenzi +4
This work proposes a unified Hierarchical Model Predictive Control (H-MPC) for modular manipulators across various morphologies, as the controller can adapt to different configurat…