22 citations · 43 across the 11 of their papers we have counts for
11 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…
INTENTION: Inferring Tendencies of Humanoid Robot Motion Through Interactive Intuition and Grounded VLM
Jin Wang, Weijie Wang, Boyuan Deng +3
Traditional control and planning for robotic manipulation heavily rely on precise physical models and predefined action sequences. While effective in structured environments, such…
Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learning
Ioannis Dadiotis, Mayank Mittal, Nikos Tsagarakis +1
Non-prehensile pushing to move and reorient objects to a goal is a versatile loco-manipulation skill. In the real world, the object's physical properties and friction with the floo…
Grounding Language Models in Autonomous Loco-manipulation Tasks
Jin Wang, Nikos Tsagarakis
Humanoid robots with behavioral autonomy have consistently been regarded as ideal collaborators in our daily lives and promising representations of embodied intelligence. Compared…
Task-Driven Computational Framework for Simultaneously Optimizing Design and Mounted Pose of Modular Reconfigurable Manipulators
Maolin Lei, Edoardo Romiti, Arturo Laurenz +1
Modular reconfigurable manipulators enable quick adaptation and versatility to address different application environments and tailor to the specific requirements of the tasks. Task…
Whole-body MPC for highly redundant legged manipulators: experimental evaluation with a 37 DoF dual-arm quadruped
Ioannis Dadiotis, Arturo Laurenzi, Nikos Tsagarakis
Recent progress in legged locomotion has rendered quadruped manipulators a promising solution for performing tasks that require both mobility and manipulation (loco-manipulation).…
Design and Validation of a Multi-Arm Relocatable Manipulator for Space Applications
Enrico Mingo Hoffman, Arturo Laurenzi, Francesco Ruscelli +13
This work presents the computational design and validation of the Multi-Arm Relocatable Manipulator (MARM), a three-limb robot for space applications, with particular reference to…
Trajectory Optimization for Quadruped Mobile Manipulators that Carry Heavy Payload
Ioannis Dadiotis, Arturo Laurenzi, Nikos Tsagarakis
This paper presents a simplified model-based trajectory optimization (TO) formulation for motion planning on quadruped mobile manipulators that carry heavy payload of known mass. T…