177 citations · 264 across the 79 of their papers we have counts for
9 papers · 1 filter
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…
Technical Report: The Policy Graph Improvement Algorithm
Joni Pajarinen
Optimizing a partially observable Markov decision process (POMDP) policy is challenging. The policy graph improvement (PGI) algorithm for POMDPs represents the policy as a fixed si…
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…
Convex Regularization in Monte-Carlo Tree Search
Tuan Dam, Carlo D'Eramo, Jan Peters +1
Monte-Carlo planning and Reinforcement Learning (RL) are essential to sequential decision making. The recent AlphaGo and AlphaZero algorithms have shown how to successfully combine…
Machine Learning Based Mobile Network Throughput Classification
Lauri Alho, Adrian Burian, Janne Helenius +1
Identifying mobile network problems in 4G cells is more challenging when the complexity of the network increases, and privacy concerns limit the information content of the data. Th…
Self-Paced Deep Reinforcement Learning
Pascal Klink, Carlo D'Eramo, Jan Peters +1
Curriculum reinforcement learning (CRL) improves the learning speed and stability of an agent by exposing it to a tailored series of tasks throughout learning. Despite empirical su…