382 citations · 1.2k across the 106 of their papers we have counts for
22 papers · 1 filter
Model-Based Reinforcement Learning via Stochastic Hybrid Models
Hany Abdulsamad, Jan Peters
Optimal control of general nonlinear systems is a central challenge in automation. Enabled by powerful function approximators, data-driven approaches to control have recently succe…
Robot Learning from Randomized Simulations: A Review
Fabio Muratore, Fabio Ramos, Greg Turk +3
The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data. Unfortunately, it is prohibitively expensive to gen…
A Differentiable Newton-Euler Algorithm for Real-World Robotics
Michael Lutter, Johannes Silberbauer, Joe Watson +1
Obtaining dynamics models is essential for robotics to achieve accurate model-based controllers and simulators for planning. The dynamics models are typically obtained using model…
Learning Stable Vector Fields on Lie Groups
Julen Urain, Davide Tateo, Jan Peters
Learning robot motions from demonstration requires models able to specify vector fields for the full robot pose when the task is defined in operational space. Recent advances in re…
Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models
Michael Lutter, Jan Peters
Deep learning has been widely used within learning algorithms for robotics. One disadvantage of deep networks is that these networks are black-box representations. Therefore, the l…
Real Robot Challenge: A Robotics Competition in the Cloud
Stefan Bauer, Felix Widmaier, Manuel Wüthrich +39
Dexterous manipulation remains an open problem in robotics. To coordinate efforts of the research community towards tackling this problem, we propose a shared benchmark. We designe…
An Empirical Analysis of Measure-Valued Derivatives for Policy Gradients
João Carvalho, Davide Tateo, Fabio Muratore +1
Reinforcement learning methods for robotics are increasingly successful due to the constant development of better policy gradient techniques. A precise (low variance) and accurate…
Efficient and Reactive Planning for High Speed Robot Air Hockey
Puze Liu, Davide Tateo, Haitham Bou-Ammar +1
Highly dynamic robotic tasks require high-speed and reactive robots. These tasks are particularly challenging due to the physical constraints, hardware limitations, and the high un…
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval +9
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces. By…
Robust Value Iteration for Continuous Control Tasks
Michael Lutter, Shie Mannor, Jan Peters +2
When transferring a control policy from simulation to a physical system, the policy needs to be robust to variations in the dynamics to perform well. Commonly, the optimal policy o…
Evolutionary Training and Abstraction Yields Algorithmic Generalization of Neural Computers
Daniel Tanneberg, Elmar Rueckert, Jan Peters
A key feature of intelligent behaviour is the ability to learn abstract strategies that scale and transfer to unfamiliar problems. An abstract strategy solves every sample from a p…
Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning
Julen Urain, Anqi Li, Puze Liu +2
Reactive motion generation problems are usually solved by computing actions as a sum of policies. However, these policies are independent of each other and thus, they can have conf…
Value Iteration in Continuous Actions, States and Time
Michael Lutter, Shie Mannor, Jan Peters +2
Classical value iteration approaches are not applicable to environments with continuous states and actions. For such environments, the states and actions are usually discretized, w…
Benchmarking Structured Policies and Policy Optimization for Real-World Dexterous Object Manipulation
Niklas Funk, Charles Schaff, Rishabh Madan +10
Dexterous manipulation is a challenging and important problem in robotics. While data-driven methods are a promising approach, current benchmarks require simulation or extensive en…
Reinforcement Learning using Guided Observability
Stephan Weigand, Pascal Klink, Jan Peters +1
Due to recent breakthroughs, reinforcement learning (RL) has demonstrated impressive performance in challenging sequential decision-making problems. However, an open question is ho…
SKID RAW: Skill Discovery from Raw Trajectories
Daniel Tanneberg, Kai Ploeger, Elmar Rueckert +1
Integrating robots in complex everyday environments requires a multitude of problems to be solved. One crucial feature among those is to equip robots with a mechanism for teaching…
Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning
Andrew S. Morgan, Daljeet Nandha, Georgia Chalvatzaki +3
Substantial advancements to model-based reinforcement learning algorithms have been impeded by the model-bias induced by the collected data, which generally hurts performance. Mean…
Learning Human-like Hand Reaching for Human-Robot Handshaking
Vignesh Prasad, Ruth Stock-Homburg, Jan Peters
One of the first and foremost non-verbal interactions that humans perform is a handshake. It has an impact on first impressions as touch can convey complex emotions. This makes han…
Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk
Joe Watson, Jan Peters
Discrete-time stochastic optimal control remains a challenging problem for general, nonlinear systems under significant uncertainty, with practical solvers typically relying on the…
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative Entropy Trust-Regions
Hany Abdulsamad, Tim Dorau, Boris Belousov +2
Trajectory optimization and model predictive control are essential techniques underpinning advanced robotic applications, ranging from autonomous driving to full-body humanoid cont…
Extended Tree Search for Robot Task and Motion Planning
Tianyu Ren, Georgia Chalvatzaki, Jan Peters
Integrated task and motion planning (TAMP) is desirable for generalized autonomy robots but it is challenging at the same time. TAMP requires the planner to not only search in both…
Human-Robot Handshaking: A Review
Vignesh Prasad, Ruth Stock-Homburg, Jan Peters
For some years now, the use of social, anthropomorphic robots in various situations has been on the rise. These are robots developed to interact with humans and are equipped with c…