177 citations · 264 across the 80 of their papers we have counts for
4 papers · 2 filters
POMDP Manipulation Planning under Object Composition Uncertainty
Joni Pajarinen, Jens Lundell, Ville Kyrki
Manipulating unknown objects in a cluttered environment is difficult because segmentation of the scene into objects, that is, object composition is uncertain. Due to this uncertain…
Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization
Mikko Lauri, Joni Pajarinen, Jan Peters +1
3D scene models are useful in robotics for tasks such as path planning, object manipulation, and structural inspection. We consider the problem of creating a 3D model using depth i…
Deep Adversarial Reinforcement Learning for Object Disentangling
Melvin Laux, Oleg Arenz, Jan Peters +1
Deep learning in combination with improved training techniques and high computational power has led to recent advances in the field of reinforcement learning (RL) and to successful…
Probabilistic approach to physical object disentangling
Joni Pajarinen, Oleg Arenz, Jan Peters +1
Physically disentangling entangled objects from each other is a problem encountered in waste segregation or in any task that requires disassembly of structures. Often there are no…