382 citations · 1.2k across the 106 of their papers we have counts for
26 papers · 1 filter
Structured Policy Representation: Imposing Stability in arbitrarily conditioned dynamic systems
Julen Urain, Davide Tateo, Tianyu Ren +1
We present a new family of deep neural network-based dynamic systems. The presented dynamics are globally stable and can be conditioned with an arbitrary context state. We show how…
Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop
Sebastian Höfer, Kostas Bekris, Ankur Handa +12
This report presents the debates, posters, and discussions of the Sim2Real workshop held in conjunction with the 2020 edition of the "Robotics: Science and System" conference. Twel…
Differentiable Implicit Layers
Andreas Look, Simona Doneva, Melih Kandemir +2
In this paper, we introduce an efficient backpropagation scheme for non-constrained implicit functions. These functions are parametrized by a set of learnable weights and may optio…
Convex Optimization with an Interpolation-based Projection and its Application to Deep Learning
Riad Akrour, Asma Atamna, Jan Peters
Convex optimizers have known many applications as differentiable layers within deep neural architectures. One application of these convex layers is to project points into a convex…
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning
Michael Lutter, Johannes Silberbauer, Joe Watson +1
A limitation of model-based reinforcement learning (MBRL) is the exploitation of errors in the learned models. Black-box models can fit complex dynamics with high fidelity, but the…
A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning
Hany Abdulsamad, Peter Nickl, Pascal Klink +1
Probabilistic regression techniques in control and robotics applications have to fulfill different criteria of data-driven adaptability, computational efficiency, scalability to hi…
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards
Kai Ploeger, Michael Lutter, Jan Peters
Robots that can learn in the physical world will be important to en-able robots to escape their stiff and pre-programmed movements. For dynamic high-acceleration tasks, such as jug…
ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows
Julen Urain, Michelle Ginesi, Davide Tateo +1
We introduce ImitationFlow, a novel Deep generative model that allows learning complex globally stable, stochastic, nonlinear dynamics. Our approach extends the Normalizing Flows f…
A Differentiable Newton Euler Algorithm for Multi-body Model Learning
Michael Lutter, Johannes Silberbauer, Joe Watson +1
In this work, we examine a spectrum of hybrid model for the domain of multi-body robot dynamics. We motivate a computation graph architecture that embodies the Newton Euler equatio…
Batch Reinforcement Learning with a Nonparametric Off-Policy Policy Gradient
Samuele Tosatto, João Carvalho, Jan Peters
Off-policy Reinforcement Learning (RL) holds the promise of better data efficiency as it allows sample reuse and potentially enables safe interaction with the environment. Current…
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills
Samuele Tosatto, Georgia Chalvatzaki, Jan Peters
Parameterized movement primitives have been extensively used for imitation learning of robotic tasks. However, the high-dimensionality of the parameter space hinders the improvemen…
Active Inference or Control as Inference? A Unifying View
Joe Watson, Abraham Imohiosen, Jan Peters
Active inference (AI) is a persuasive theoretical framework from computational neuroscience that seeks to describe action and perception as inference-based computation. However, th…
Advances in Human-Robot Handshaking
Vignesh Prasad, Ruth Stock-Homburg, Jan Peters
The use of social, anthropomorphic robots to support humans in various industries has been on the rise. During Human-Robot Interaction (HRI), physically interactive non-verbal beha…
Assisted Teleoperation in Changing Environments with a Mixture of Virtual Guides
Marco Ewerton, Oleg Arenz, Jan Peters
Haptic guidance is a powerful technique to combine the strengths of humans and autonomous systems for teleoperation. The autonomous system can provide haptic cues to enable the ope…
Model-Based Quality-Diversity Search for Efficient Robot Learning
Leon Keller, Daniel Tanneberg, Svenja Stark +1
Despite recent progress in robot learning, it still remains a challenge to program a robot to deal with open-ended object manipulation tasks. One approach that was recently used to…
Underactuated Waypoint Trajectory Optimization for Light Painting Photography
Christian Eilers, Jonas Eschmann, Robin Menzenbach +3
Despite their abundance in robotics and nature, underactuated systems remain a challenge for control engineering. Trajectory optimization provides a generally applicable solution,…
Data-efficient Domain Randomization with Bayesian Optimization
Fabio Muratore, Christian Eilers, Michael Gienger +1
When learning policies for robot control, the required real-world data is typically prohibitively expensive to acquire, so learning in simulation is a popular strategy. Unfortunate…
Dimensionality Reduction of Movement Primitives in Parameter Space
Samuele Tosatto, Jonas Stadtmueller, Jan Peters
Movement primitives are an important policy class for real-world robotics. However, the high dimensionality of their parametrization makes the policy optimization expensive both in…
Metric-Based Imitation Learning Between Two Dissimilar Anthropomorphic Robotic Arms
Marcus Ebner von Eschenbach, Binyamin Manela, Jan Peters +1
The development of autonomous robotic systems that can learn from human demonstrations to imitate a desired behavior - rather than being manually programmed - has huge technologica…
A Probabilistic Framework for Imitating Human Race Driver Behavior
Stefan Löckel, Jan Peters, Peter van Vliet
Understanding and modeling human driver behavior is crucial for advanced vehicle development. However, unique driving styles, inconsistent behavior, and complex decision processes…
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…
An Upper Bound of the Bias of Nadaraya-Watson Kernel Regression under Lipschitz Assumptions
Samuele Tosatto, Riad Akrour, Jan Peters
The Nadaraya-Watson kernel estimator is among the most popular nonparameteric regression technique thanks to its simplicity. Its asymptotic bias has been studied by Rosenblatt in 1…
Evaluation of the Handshake Turing Test for anthropomorphic Robots
Ruth Stock-Homburg, Jan Peters, Katharina Schneider +2
Handshakes are fundamental and common greeting and parting gestures among humans. They are important in shaping first impressions as people tend to associate character traits with…
MushroomRL: Simplifying Reinforcement Learning Research
Carlo D'Eramo, Davide Tateo, Andrea Bonarini +2
MushroomRL is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments. Compared to other available libra…