19 citations · 24 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
A Task Allocation Framework for Human Multi-Robot Collaborative Settings
Martina Lippi, Paolo Di Lillo, Alessandro Marino
The requirements of modern production systems together with more advanced robotic technologies have fostered the integration of teams comprising humans and autonomous robots. Howev…
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…
Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning
Constantinos Chamzas, Martina Lippi, Michael C. Welle +3
Learning state representations enables robotic planning directly from raw observations such as images. Most methods learn state representations by utilizing losses based on the rec…
Safety in human-multi robot collaborative scenarios: a trajectory scaling approach
Martina Lippi, Alessandro Marino
In this paper, a strategy to handle the human safety in a multi-robot scenario is devised. In the presented framework, it is foreseen that robots are in charge of performing any co…
A Data-Driven Approach for Contact Detection, Classification and Reaction in Physical Human-Robot Collaboration
Martina Lippi, Giuseppe Gillini, Alessandro Marino +1
This paper considers a scenario where a robot and a human operator share the same workspace, and the robot is able to both carry out autonomous tasks and physically interact with t…
A Mixed-Integer Linear Programming Formulation for Human Multi-Robot Task Allocation
Martina Lippi, Alessandro Marino
In this work, we address a task allocation problem for human multi-robot settings. Given a set of tasks to perform, we formulate a general Mixed-Integer Linear Programming (MILP) p…
Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap
Martina Lippi, Petra Poklukar, Michael C. Welle +4
We present a framework for visual action planning of complex manipulation tasks with high-dimensional state spaces, focusing on manipulation of deformable objects. We propose a Lat…
Latent Space Roadmap for Visual Action Planning of Deformable and Rigid Object Manipulation
Martina Lippi, Petra Poklukar, Michael C. Welle +4
We present a framework for visual action planning of complex manipulation tasks with high-dimensional state spaces such as manipulation of deformable objects. Planning is performed…