103 citations · 134 across the 10 of their papers we have counts for
10 papers
KDPE: A Kernel Density Estimation Strategy for Diffusion Policy Trajectory Selection
Andrea Rosasco, Federico Ceola, Giulia Pasquale +1
Learning robot policies that capture multimodality in the training data has been a long-standing open challenge for behavior cloning. Recent approaches tackle the problem by modeli…
PCHands: PCA-based Hand Pose Synergy Representation on Manipulators with N-DoF
En Yen Puang, Federico Ceola, Giulia Pasquale +1
We consider the problem of learning a common representation for dexterous manipulation across manipulators of different morphologies. To this end, we propose PCHands, a novel appro…
HannesImitation: Grasping with the Hannes Prosthetic Hand via Imitation Learning
Carlo Alessi, Federico Vasile, Federico Ceola +3
Recent advancements in control of prosthetic hands have focused on increasing autonomy through the use of cameras and other sensory inputs. These systems aim to reduce the cognitiv…
RESPRECT: Speeding-up Multi-fingered Grasping with Residual Reinforcement Learning
Federico Ceola, Lorenzo Rosasco, Lorenzo Natale
Deep Reinforcement Learning (DRL) has proven effective in learning control policies using robotic grippers, but much less practical for solving the problem of grasping with dextero…
LHManip: A Dataset for Long-Horizon Language-Grounded Manipulation Tasks in Cluttered Tabletop Environments
Federico Ceola, Lorenzo Natale, Niko Sünderhauf +1
Instructing a robot to complete an everyday task within our homes has been a long-standing challenge for robotics. While recent progress in language-conditioned imitation learning…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
A Grasp Pose is All You Need: Learning Multi-fingered Grasping with Deep Reinforcement Learning from Vision and Touch
Federico Ceola, Elisa Maiettini, Lorenzo Rosasco +1
Multi-fingered robotic hands have potential to enable robots to perform sophisticated manipulation tasks. However, teaching a robot to grasp objects with an anthropomorphic hand is…
Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot
Federico Ceola, Elisa Maiettini, Giulia Pasquale +3
The visual system of a robot has different requirements depending on the application: it may require high accuracy or reliability, be constrained by limited resources or need fast…
Fast Object Segmentation Learning with Kernel-based Methods for Robotics
Federico Ceola, Elisa Maiettini, Giulia Pasquale +2
Object segmentation is a key component in the visual system of a robot that performs tasks like grasping and object manipulation, especially in presence of occlusions. Like many ot…
Fast Region Proposal Learning for Object Detection for Robotics
Federico Ceola, Elisa Maiettini, Giulia Pasquale +2
Object detection is a fundamental task for robots to operate in unstructured environments. Today, there are several deep learning algorithms that solve this task with remarkable pe…