activity
20192025
most citedThe Treachery of Images: Bayesian Scene Keypoints for Deep Policy Learning in Robotic Manipulation

11 citations · 18 across the 9 of their papers we have counts for

collaborators

11 papers

cs.RO2025

Robotic Task Ambiguity Resolution via Natural Language Interaction

Eugenio Chisari, Jan Ole von Hartz, Fabien Despinoy +1

Language-conditioned policies have recently gained substantial adoption in robotics as they allow users to specify tasks using natural language, making them highly versatile. While…

cs.RO2024★ 1 cited

Learning Robotic Manipulation Policies from Point Clouds with Conditional Flow Matching

Eugenio Chisari, Nick Heppert, Max Argus +3

Learning from expert demonstrations is a promising approach for training robotic manipulation policies from limited data. However, imitation learning algorithms require a number of…

cs.RO2024

CenterArt: Joint Shape Reconstruction and 6-DoF Grasp Estimation of Articulated Objects

Sassan Mokhtar, Eugenio Chisari, Nick Heppert +1

Precisely grasping and reconstructing articulated objects is key to enabling general robotic manipulation. In this paper, we propose CenterArt, a novel approach for simultaneous 3D…

cs.RO2024★ 1 cited

PseudoTouch: Efficiently Imaging the Surface Feel of Objects for Robotic Manipulation

Adrian Röfer, Nick Heppert, Abdallah Ayad +2

Tactile sensing is vital for human dexterous manipulation, however, it has not been widely used in robotics. Compact, low-cost sensing platforms can facilitate a change, but unlike…

cs.RO2023★ 1 cited

CenterGrasp: Object-Aware Implicit Representation Learning for Simultaneous Shape Reconstruction and 6-DoF Grasp Estimation

Eugenio Chisari, Nick Heppert, Tim Welschehold +2

Reliable object grasping is a crucial capability for autonomous robots. However, many existing grasping approaches focus on general clutter removal without explicitly modeling obje…

cs.RO2023★ 11 cited

The Treachery of Images: Bayesian Scene Keypoints for Deep Policy Learning in Robotic Manipulation

Jan Ole von Hartz, Eugenio Chisari, Tim Welschehold +3

In policy learning for robotic manipulation, sample efficiency is of paramount importance. Thus, learning and extracting more compact representations from camera observations is a…