4 citations · 6 across the 7 of their papers we have counts for
6 papers · 1 filter
Sampling-Based Coordination-Informed Multi-Objective Multi-Robot Reinforcement Learning
Antonio Marino, Esteban Restrepo, Soon-jo Chung +2
Multi-robot systems must simultaneously optimize competing objectives while maintaining coordinated behavior. Existing multi-agent reinforcement learning approaches often rely on f…
Impact-Robust Posture Optimization for Aerial Manipulation
Amr Afifi, Ahmad Gazar, Javier Alonso-Mora +2
We present a novel method for optimizing the posture of kinematically redundant torque-controlled robots to improve robustness during impacts. A rigid impact model is used as the b…
Neural Style Transfer with Twin-Delayed DDPG for Shared Control of Robotic Manipulators
Raul Fernandez-Fernandez, Marco Aggravi, Paolo Robuffo Giordano +2
Neural Style Transfer (NST) refers to a class of algorithms able to manipulate an element, most often images, to adopt the appearance or style of another one. Each element is defin…
Input State Stability of Gated Graph Neural Networks
Antonio Marino, Claudio Pacchierotti, Paolo Robuffo Giordano
In this paper, we aim to find the conditions for input-state stability (ISS) and incremental input-state stability (ISS) of Gated Graph Neural Networks (GGNNs). We show that thi…
COP: Control & Observability-aware Planning
Christoph Böhm, Pascal Brault, Quentin Delamare +2
In this research, we aim to answer the question: How to combine Closed-Loop State and Input Sensitivity-based with Observability-aware trajectory planning? These possibly opposite…
Human-in-the-loop optimisation: mixed initiative grasping for optimally facilitating post-grasp manipulative actions
Amir M. Ghalamzan Esfahani, Firas Abi-Farraj, Paolo Robuffo Giordano +1
This paper addresses the problem of mixed initiative, shared control for master-slave grasping and manipulation. We propose a novel system, in which an autonomous agent assists a h…