62 citations · 148 across the 29 of their papers we have counts for
6 papers · 1 filter
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…
Robot Skill Generalization via Keypoint Integrated Soft Actor-Critic Gaussian Mixture Models
Iman Nematollahi, Kirill Yankov, Wolfram Burgard +1
A long-standing challenge for a robotic manipulation system operating in real-world scenarios is adapting and generalizing its acquired motor skills to unseen environments. We tack…
Learning Hierarchical Interactive Multi-Object Search for Mobile Manipulation
Fabian Schmalstieg, Daniel Honerkamp, Tim Welschehold +1
Existing object-search approaches enable robots to search through free pathways, however, robots operating in unstructured human-centered environments frequently also have to manip…
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…
Improving Deep Dynamics Models for Autonomous Vehicles with Multimodal Latent Mapping of Surfaces
Johan Vertens, Nicolai Dorka, Tim Welschehold +2
The safe deployment of autonomous vehicles relies on their ability to effectively react to environmental changes. This can require maneuvering on varying surfaces which is still a…
Dynamic Update-to-Data Ratio: Minimizing World Model Overfitting
Nicolai Dorka, Tim Welschehold, Wolfram Burgard
Early stopping based on the validation set performance is a popular approach to find the right balance between under- and overfitting in the context of supervised learning. However…