Jan Peters

Convex Optimization with an Interpolation-based Projection and its Application to Deep Learning

Convex optimizers have known many applications as differentiable layers ...
11/13/2020 ∙ by Riad Akrour, et al. ∙ 0 ∙ share

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A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning

Probabilistic regression techniques in control and robotics applications...
11/10/2020 ∙ by Hany Abdulsamad, et al. ∙ 13 ∙ share

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Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning

A limitation of model-based reinforcement learning (MBRL) is the exploit...
11/03/2020 ∙ by Michael Lutter, et al. ∙ 0 ∙ share

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Batch Reinforcement Learning with a Nonparametric Off-Policy Policy Gradient

Off-policy Reinforcement Learning (RL) holds the promise of better data ...
10/27/2020 ∙ by Samuele Tosatto, et al. ∙ 15 ∙ share

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Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills

Parameterized movement primitives have been extensively used for imitati...
10/26/2020 ∙ by Samuele Tosatto, et al. ∙ 1 ∙ share

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High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards

Robots that can learn in the physical world will be important to en-able...
10/26/2020 ∙ by Kai Ploeger, et al. ∙ 0 ∙ share

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ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows

We introduce ImitationFlow, a novel Deep generative model that allows le...
10/25/2020 ∙ by Julen Urain, et al. ∙ 0 ∙ share

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A Differentiable Newton Euler Algorithm for Multi-body Model Learning

In this work, we examine a spectrum of hybrid model for the domain of mu...
10/19/2020 ∙ by Michael Lutter, et al. ∙ 0 ∙ share

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Differentiable Implicit Layers

In this paper, we introduce an efficient backpropagation scheme for non-...
10/14/2020 ∙ by Andreas Look, et al. ∙ 0 ∙ share

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Active Inference or Control as Inference? A Unifying View

Active inference (AI) is a persuasive theoretical framework from computa...
10/01/2020 ∙ by Joe Watson, et al. ∙ 6 ∙ share

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Advances in Human-Robot Handshaking

The use of social, anthropomorphic robots to support humans in various i...
08/26/2020 ∙ by Vignesh Prasad, et al. ∙ 0 ∙ share

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Assisted Teleoperation in Changing Environments with a Mixture of Virtual Guides

Haptic guidance is a powerful technique to combine the strengths of huma...
08/12/2020 ∙ by Marco Ewerton, et al. ∙ 0 ∙ share

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Model-Based Quality-Diversity Search for Efficient Robot Learning

Despite recent progress in robot learning, it still remains a challenge ...
08/11/2020 ∙ by Leon Keller, et al. ∙ 0 ∙ share

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Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization

3D scene models are useful in robotics for tasks such as path planning, ...
07/04/2020 ∙ by Mikko Lauri, et al. ∙ 2 ∙ share

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Convex Regularization in Monte-Carlo Tree Search

Monte-Carlo planning and Reinforcement Learning (RL) are essential to se...
07/01/2020 ∙ by Tuan Dam, et al. ∙ 0 ∙ share

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Deterministic Inference of Neural Stochastic Differential Equations

Model noise is known to have detrimental effects on neural networks, suc...
06/16/2020 ∙ by Andreas Look, et al. ∙ 0 ∙ share

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Learning to Play Table Tennis From Scratch using Muscular Robots

Dynamic tasks like table tennis are relatively easy to learn for humans ...
06/10/2020 ∙ by Dieter Büchler, et al. ∙ 0 ∙ share

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Reinforcement Learning from a Mixture of Interpretable Experts

Reinforcement learning (RL) has demonstrated its ability to solve high d...
06/10/2020 ∙ by Riad Akrour, et al. ∙ 0 ∙ share

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Orientation Attentive Robot Grasp Synthesis

Physical neighborhoods of grasping points in common objects may offer a ...
06/09/2020 ∙ by Nikolaos Gkanatsios, et al. ∙ 0 ∙ share

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

The control of nonlinear dynamical systems remains a major challenge for...
05/04/2020 ∙ by Hany Abdulsamad, et al. ∙ 8 ∙ share

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Self-Paced Deep Reinforcement Learning

Generalization and reuse of agent behaviour across a variety of learning...
04/24/2020 ∙ by Pascal Klink, et al. ∙ 0 ∙ share

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Deep Reinforcement Learning with Weighted Q-Learning

Overestimation of the maximum action-value is a well-known problem that ...
03/20/2020 ∙ by Andrea Cini, et al. ∙ 0 ∙ share

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Learning to Fly via Deep Model-Based Reinforcement Learning

Learning to control robots without requiring models has been a long-term...
03/19/2020 ∙ by Philip Becker-Ehmck, et al. ∙ 7 ∙ share

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Deep Adversarial Reinforcement Learning for Object Disentangling

Deep learning in combination with improved training techniques and high ...
03/08/2020 ∙ by Melvin Laux, et al. ∙ 0 ∙ share

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Bayesian Domain Randomization for Sim-to-Real Transfer

When learning policies for robot control, the real-world data required i...
03/05/2020 ∙ by Fabio Muratore, et al. ∙ 0 ∙ share

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Underactuated Waypoint Trajectory Optimization for Light Painting Photography

Despite their abundance in robotics and nature, underactuated systems re...
03/03/2020 ∙ by Christian Eilers, et al. ∙ 0 ∙ share

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Dimensionality Reduction of Movement Primitives in Parameter Space

Movement primitives are an important policy class for real-world robotic...
02/26/2020 ∙ by Samuele Tosatto, et al. ∙ 0 ∙ share

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Probabilistic approach to physical object disentangling

Physically disentangling entangled objects from each other is a problem ...
02/26/2020 ∙ by Joni Pajarinen, et al. ∙ 0 ∙ share

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Metric-Based Imitation Learning Between Two Dissimilar Anthropomorphic Robotic Arms

The development of autonomous robotic systems that can learn from human ...
02/25/2020 ∙ by Marcus Ebner von Eschenbach, et al. ∙ 0 ∙ share

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An Upper Bound of the Bias of Nadaraya-Watson Kernel Regression under Lipschitz Assumptions

The Nadaraya-Watson kernel estimator is among the most popular nonparame...
01/29/2020 ∙ by Samuele Tosatto, et al. ∙ 0 ∙ share

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Evaluation of the Handshake Turing Test for anthropomorphic Robots

Handshakes are fundamental and common greeting and parting gestures amon...
01/28/2020 ∙ by Ruth Stock-Homburg, et al. ∙ 0 ∙ share

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A Probabilistic Framework for Imitating Human Race Driver Behavior

Understanding and modeling human driver behavior is crucial for advanced...
01/22/2020 ∙ by Stefan Löckel, et al. ∙ 6 ∙ share

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A Nonparametric Offpolicy Policy Gradient

Reinforcement learning (RL) algorithms still suffer from high sample com...
01/08/2020 ∙ by Samuele Tosatto, et al. ∙ 0 ∙ share

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MushroomRL: Simplifying Reinforcement Learning Research

MushroomRL is an open-source Python library developed to simplify the pr...
01/04/2020 ∙ by Carlo D'Eramo, et al. ∙ 0 ∙ share

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Long-Term Visitation Value for Deep Exploration in Sparse Reward Reinforcement Learning

Reinforcement learning with sparse rewards is still an open challenge. C...
01/01/2020 ∙ by Simone Parisi, et al. ∙ 7 ∙ share

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Generalized Mean Estimation in Monte-Carlo Tree Search

We consider Monte-Carlo Tree Search (MCTS) applied to Markov Decision Pr...
11/01/2019 ∙ by Tuan Dam, et al. ∙ 0 ∙ share

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Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer

A key feature of intelligent behavior is the ability to learn abstract s...
10/30/2019 ∙ by Daniel Tanneberg, et al. ∙ 0 ∙ share

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Receding Horizon Curiosity

Sample-efficient exploration is crucial not only for discovering rewardi...
10/08/2019 ∙ by Matthias Schultheis, et al. ∙ 0 ∙ share

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Stochastic Optimal Control as Approximate Input Inference

Optimal control of stochastic nonlinear dynamical systems is a major cha...
10/07/2019 ∙ by Joe Watson, et al. ∙ 0 ∙ share

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Self-Paced Contextual Reinforcement Learning

Generalization and adaptation of learned skills to novel situations is a...
10/07/2019 ∙ by Pascal Klink, et al. ∙ 0 ∙ share

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Building a Library of Tactile Skills Based on FingerVision

Camera-based tactile sensors are emerging as a promising inexpensive sol...
09/20/2019 ∙ by Boris Belousov, et al. ∙ 0 ∙ share

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HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints

Learning optimal feedback control laws capable of executing optimal traj...
09/13/2019 ∙ by Michael Lutter, et al. ∙ 0 ∙ share

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Real Time Trajectory Prediction Using Deep Conditional Generative Models

Data driven methods for time series forecasting that quantify uncertaint...
09/09/2019 ∙ by Sebastián Gómez-González, et al. ∙ 1 ∙ share

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Reliable Real Time Ball Tracking for Robot Table Tennis

Robot table tennis systems require a vision system that can track the ba...
08/20/2019 ∙ by Sebastián Gómez-González, et al. ∙ 0 ∙ share

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Model-based Lookahead Reinforcement Learning

Model-based Reinforcement Learning (MBRL) allows data-efficient learning...
08/15/2019 ∙ by Zhang-Wei Hong, et al. ∙ 0 ∙ share

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Experience Reuse with Probabilistic Movement Primitives

Acquiring new robot motor skills is cumbersome, as learning a skill from...
08/11/2019 ∙ by Svenja Stark, et al. ∙ 0 ∙ share

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Assessing Transferability from Simulation to Reality for Reinforcement Learning

Learning robot control policies from physics simulations is of great int...
07/10/2019 ∙ by Fabio Muratore, et al. ∙ 0 ∙ share

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Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

Deep learning has achieved astonishing results on many tasks with large ...
07/10/2019 ∙ by Michael Lutter, et al. ∙ 0 ∙ share

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Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems

Applying Deep Learning to control has a lot of potential for enabling th...
07/10/2019 ∙ by Michael Lutter, et al. ∙ 0 ∙ share

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Entropic Regularization of Markov Decision Processes

An optimal feedback controller for a given Markov decision process (MDP)...
07/06/2019 ∙ by Boris Belousov, et al. ∙ 1 ∙ share

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Jan Peters

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