4 citations · 7 across the 8 of their papers we have counts for
8 papers
On the Generalization Capabilities, Design Choices and Limitations of Keypoint Imitation Learning
Thomas Lips, Marco Moletta, Michael C. Welle +2
RGB-based imitation learning requires many demonstrations to generalize to unseen objects or scenes, motivating research into intermediate representations to improve generalization…
Preference Aligned Visuomotor Diffusion Policies for Deformable Object Manipulation
Marco Moletta, Michael C. Welle, Danica Kragic
Humans naturally develop preferences for how manipulation tasks should be performed, which are often subtle, personal, and difficult to articulate. Although it is important for rob…
Real-Time Operator Takeover for Visuomotor Diffusion Policy Training
Marco Moletta, Michael C. Welle, Nils Ingelhag +2
We present a Real-Time Operator Takeover (RTOT) paradigm that enables operators to seamlessly take control of a live visuomotor diffusion policy, guiding the system back to desirab…
Unfolding the Literature: A Review of Robotic Cloth Manipulation
Alberta Longhini, Yufei Wang, Irene Garcia-Camacho +9
The realm of textiles spans clothing, households, healthcare, sports, and industrial applications. The deformable nature of these objects poses unique challenges that prior work on…
Visual Action Planning with Multiple Heterogeneous Agents
Martina Lippi, Michael C. Welle, Marco Moletta +3
Visual planning methods are promising to handle complex settings where extracting the system state is challenging. However, none of the existing works tackles the case of multiple…
A Virtual Reality Framework for Human-Robot Collaboration in Cloth Folding
Marco Moletta, Maciej K. Wozniak, Michael C. Welle +1
We present a virtual reality (VR) framework to automate the data collection process in cloth folding tasks. The framework uses skeleton representations to help the user define the…
EDO-Net: Learning Elastic Properties of Deformable Objects from Graph Dynamics
Alberta Longhini, Marco Moletta, Alfredo Reichlin +4
We study the problem of learning graph dynamics of deformable objects that generalizes to unknown physical properties. Our key insight is to leverage a latent representation of ela…
Elastic Context: Encoding Elasticity for Data-driven Models of Textiles
Alberta Longhini, Marco Moletta, Alfredo Reichlin +6
Physical interaction with textiles, such as assistive dressing, relies on advanced dextreous capabilities. The underlying complexity in textile behavior when being pulled and stret…