activity
20192021
most citedMulti-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization

37 citations · 86 across the 4 of their papers we have counts for

collaborators

11 papers

cs.LG2021

A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning

Pascal Klink, Hany Abdulsamad, Boris Belousov +3

Across machine learning, the use of curricula has shown strong empirical potential to improve learning from data by avoiding local optima of training objectives. For reinforcement…

cs.RO202037 cited

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…

cs.LG2020

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…

cs.RO202011 cited

Learning to Play Table Tennis From Scratch using Muscular Robots

Dieter Büchler, Simon Guist, Roberto Calandra +3

Dynamic tasks like table tennis are relatively easy to learn for humans but pose significant challenges to robots. Such tasks require accurate control of fast movements and precise…

cs.RO2020

Orientation Attentive Robotic Grasp Synthesis with Augmented Grasp Map Representation

Georgia Chalvatzaki, Nikolaos Gkanatsios, Petros Maragos +1

Inherent morphological characteristics in objects may offer a wide range of plausible grasping orientations that obfuscates the visual learning of robotic grasping. Existing grasp…

cs.LG20206 cited

Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation

Hany Abdulsamad, Jan Peters

The control of nonlinear dynamical systems remains a major challenge for autonomous agents. Current trends in reinforcement learning (RL) focus on complex representations of dynami…