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Federico Ceola

7 accepted papers

2025

HannesImitation: Grasping with the Hannes Prosthetic Hand via Imitation Learning

IROS 2025

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 cognitive load on the user by automatically controlling certain degrees of freedom. In robotics, imitation learning has emerged

Cited by 1SourcecodeScholar
2025

KDPE: A Kernel Density Estimation Strategy for Diffusion Policy Trajectory Selection

CoRL 2025poster

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 modeling the conditional action distribution with generative models. One of these approaches is Diffusion Policy, which relies…

Cited by 0SourcecodeScholar
2024

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration

ICRA 2024

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for man

Cited by 910SourcecodeScholar
2024

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0

ICRA 2024poster

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for man…

Cited by 259SourcecodeScholar
2024

RESPRECT: Speeding-up Multi-Fingered Grasping With Residual Reinforcement Learning

RA-L 2024

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 dexterous hands – especially on real robotic platforms – due to the high dimensionality of the problem. In this work, we focus

Cited by 11SourcecodeScholar
2023

A Grasp Pose is All You Need: Learning Multi-Fingered Grasping with Deep Reinforcement Learning from Vision and Touch

IROS 2023poster

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 an arduous problem due to the high dimensionality of state and action spaces. Deep Reinforcement Learning (DRL) offers…

Cited by 5SourcecodeScholar
2021

Fast Object Segmentation Learning with Kernel-based Methods for Robotics

ICRA 2021poster

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 other computer vision tasks, the adoption of deep architectures has made available algorithms that perform this task with…

Cited by 11SourcecodeScholar