14 citations · 24 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms
Ali Ghadirzadeh, Xi Chen, Petra Poklukar +3
Reinforcement learning methods can achieve significant performance but require a large amount of training data collected on the same robotic platform. A policy trained with expensi…
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…
Data-efficient visuomotor policy training using reinforcement learning and generative models
Ali Ghadirzadeh, Petra Poklukar, Ville Kyrki +2
We present a data-efficient framework for solving visuomotor sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable g…
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…