5 citations · 15 across the 27 of their papers we have counts for
30 papers
Towards Safe Reinforcement Learning with Reduced Conservativeness: A Case Study on Drone Flight Control
Loizos Hadjiloizou, Michael C. Welle, Hang Yin +1
Incorporating formal methods into reinforcement learning (RL) has the potential to result in the best of both worlds, combining the robustness of formal guarantees with the adaptab…
One Body, Two Minds: Variable Autonomy Approach for a Co-embodied Robotic Hand
Piotr Koczy, Yuchong Zhang, Danica Kragic +1
Assistive robotic systems face a fundamental trade-off: fully autonomous systems lack user agency, while fully user-controlled systems demand continuous cognitive effort. Existing…
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…
Hybrid Imitation Learning: Teleoperation Augmentation Primitives that Policies Learn to Trigger
Jonne Van Haastregt, Bastian Orthmann, Michael C. Welle +2
What an operator can demonstrate bounds what imitation learning can learn. Teleoperation interfaces map the human body to the robot, so motions that are hard for a human, such as h…
A Non-Adversarial Approach to Idempotent Generative Modelling
Mohammed Al-Jaff, Giovanni Luca Marchetti, Michael C Welle +5
Idempotent Generative Networks (IGNs) are deep generative models that also function as local data manifold projectors, mapping arbitrary inputs back onto the manifold. They are tra…
Gesture First, LLM-Assisted Voice Complement: Exploring Multimodal Robot 'Puppeteer' Teleoperation Via Virtual Counterpart in Augmented Reality
Yuchong Zhang, Bastian Orthmann, Shichen Ji +3
Robot teleoperation via augmented reality (AR) offers a promising path toward more intuitive human-robot interaction (HRI). We present a head-mounted AR 'puppeteer' system in which…
Learning Dexterous In-Hand Manipulation with Multifingered Hands via Visuomotor Diffusion
Piotr Koczy, Michael C. Welle, Danica Kragic
We present a framework for learning dexterous in-hand manipulation with multifingered hands using visuomotor diffusion policies. Our system enables complex in-hand manipulation tas…
LLM-Driven Augmented Reality Puppeteer: Controller-Free Voice-Commanded Robot Teleoperation
Yuchong Zhang, Bastian Orthmann, Michael C. Welle +2
The integration of robotics and augmented reality (AR) presents transformative opportunities for advancing human-robot interaction (HRI) by improving usability, intuitiveness, and…
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…
Puppeteer Your Robot: Augmented Reality Leader-Follower Teleoperation
Jonne van Haastregt, Michael C. Welle, Yuchong Zhang +1
High-quality demonstrations are necessary when learning complex and challenging manipulation tasks. In this work, we introduce an approach to puppeteer a robot by controlling a vir…
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…
Low-Cost Teleoperation with Haptic Feedback through Vision-based Tactile Sensors for Rigid and Soft Object Manipulation
Martina Lippi, Michael C. Welle, Maciej K. Wozniak +2
Haptic feedback is essential for humans to successfully perform complex and delicate manipulation tasks. A recent rise in tactile sensors has enabled robots to leverage the sense o…
A Robotic Skill Learning System Built Upon Diffusion Policies and Foundation Models
Nils Ingelhag, Jesper Munkeby, Jonne van Haastregt +3
In this paper, we build upon two major recent developments in the field, Diffusion Policies for visuomotor manipulation and large pre-trained multimodal foundational models to obta…
Standardization of Cloth Objects and its Relevance in Robotic Manipulation
Irene Garcia-Camacho, Alberta Longhini, Michael Welle +3
The field of robotics faces inherent challenges in manipulating deformable objects, particularly in understanding and standardising fabric properties like elasticity, stiffness, an…
AdaFold: Adapting Folding Trajectories of Cloths via Feedback-loop Manipulation
Alberta Longhini, Michael C. Welle, Zackory Erickson +1
We present AdaFold, a model-based feedback-loop framework for optimizing folding trajectories. AdaFold extracts a particle-based representation of cloth from RGB-D images and feeds…
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges
Noémie Jaquier, Michael C. Welle, Andrej Gams +6
Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents. The core concept -- reusing prior knowledge to learn in and from novel situat…
Enabling Robot Manipulation of Soft and Rigid Objects with Vision-based Tactile Sensors
Michael C. Welle, Martina Lippi, Haofei Lu +3
Endowing robots with tactile capabilities opens up new possibilities for their interaction with the environment, including the ability to handle fragile and/or soft objects. In thi…
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…
Ensemble Latent Space Roadmap for Improved Robustness in Visual Action Planning
Martina Lippi, Michael C. Welle, Andrea Gasparri +1
Planning in learned latent spaces helps to decrease the dimensionality of raw observations. In this work, we propose to leverage the ensemble paradigm to enhance the robustness of…
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…
Augment-Connect-Explore: a Paradigm for Visual Action Planning with Data Scarcity
Martina Lippi, Michael C. Welle, Petra Poklukar +2
Visual action planning particularly excels in applications where the state of the system cannot be computed explicitly, such as manipulation of deformable objects, as it enables pl…