62 citations · 185 across the 12 of their papers we have counts for
14 papers
Sharing Knowledge in Multi-Task Deep Reinforcement Learning
Carlo D'Eramo, Davide Tateo, Andrea Bonarini +2
We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning. We leverage the assumption tha…
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…
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…
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…
Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts
Riad Akrour, Davide Tateo, Jan Peters
Reinforcement learning (RL) has demonstrated its ability to solve high dimensional tasks by leveraging non-linear function approximators. However, these successes are mostly achiev…
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…
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…
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…
Deep Reinforcement Learning with Weighted Q-Learning
Andrea Cini, Carlo D'Eramo, Jan Peters +1
Reinforcement learning algorithms based on Q-learning are driving Deep Reinforcement Learning (DRL) research towards solving complex problems and achieving super-human performance…
Learning to Fly via Deep Model-Based Reinforcement Learning
Philip Becker-Ehmck, Maximilian Karl, Jan Peters +1
Learning to control robots without requiring engineered models has been a long-term goal, promising diverse and novel applications. Yet, reinforcement learning has only achieved li…
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…
Real Time Trajectory Prediction Using Deep Conditional Generative Models
Sebastian Gomez-Gonzalez, Sergey Prokudin, Bernhard Scholkopf +1
Data driven methods for time series forecasting that quantify uncertainty open new important possibilities for robot tasks with hard real time constraints, allowing the robot syste…
Switching Linear Dynamics for Variational Bayes Filtering
Philip Becker-Ehmck, Jan Peters, Patrick van der Smagt
System identification of complex and nonlinear systems is a central problem for model predictive control and model-based reinforcement learning. Despite their complexity, such syst…