b'Jan Peters'

research

∙ 09/15/2023

Sampling-Free Probabilistic Deep State-Space Models

Many real-world dynamical systems can be described as State-Space Models...

0 Andreas Look, et al. ∙

research

∙ 08/12/2023

Value-Distributional Model-Based Reinforcement Learning

Quantifying uncertainty about a policy's long-term performance is import...

0 Carlos E. Luis, et al. ∙

research

∙ 08/03/2023

Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models

Learning priors on trajectory distributions can help accelerate robot mo...

0 João Carvalho, et al. ∙

research

∙ 05/02/2023

Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems

Graph neural networks are often used to model interacting dynamical syst...

0 Andreas Look, et al. ∙

research

∙ 03/08/2023

Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles

We propose a model predictive control approach for autonomous vehicles t...

0 Johanna Bethge, et al. ∙

research

∙ 03/07/2023

Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning

Model-based reinforcement learning is one approach to increase sample ef...

0 Daniel Palenicek, et al. ∙

research

∙ 03/01/2023

LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning

Recent methods for imitation learning directly learn a Q-function using ...

0 Firas Al-Hafez, et al. ∙

research

∙ 02/25/2023

A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors

Deployment of reinforcement learning algorithms for robotics application...

0 Shangding Gu, et al. ∙

research

∙ 02/24/2023

Model-Based Uncertainty in Value Functions

We consider the problem of quantifying uncertainty over expected cumulat...

0 Carlos E. Luis, et al. ∙

research

∙ 01/09/2023

Learning-based Design and Control for Quadrupedal Robots with Parallel-Elastic Actuators

Parallel-elastic joints can improve the efficiency and strength of robot...

0 Filip Bjelonic, et al. ∙

research

∙ 12/26/2022

Visual Tactile Sensor Based Force Estimation for Position-Force Teleoperation

Vision-based tactile sensors have gained extensive attention in the robo...

0 Yaonan Zhu, et al. ∙

research

∙ 12/09/2022

Information-Theoretic Safe Exploration with Gaussian Processes

We consider a sequential decision making task where we are not allowed t...

0 Alessandro G. Bottero, et al. ∙

research

∙ 12/04/2022

Hierarchical Policy Blending As Optimal Transport

We present hierarchical policy blending as optimal transport (HiPBOT). T...

0 An T. Le, et al. ∙

research

∙ 11/29/2022

PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison

PAC-Bayes has recently re-emerged as an effective theory with which one ...

0 Hamish Flynn, et al. ∙

research

∙ 11/26/2022

How Crucial is Transformer in Decision Transformer?

Decision Transformer (DT) is a recently proposed architecture for Reinfo...

0 Max Siebenborn, et al. ∙

research

∙ 11/02/2022

Variational Hierarchical Mixtures for Learning Probabilistic Inverse Dynamics

Well-calibrated probabilistic regression models are a crucial learning c...

23 Hany Abdulsamad, et al. ∙

research

∙ 10/23/2022

Active Exploration for Robotic Manipulation

Robotic manipulation stands as a largely unsolved problem despite signif...

0 Tim Schneider, et al. ∙

research

∙ 10/22/2022

MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction

Modeling interaction dynamics to generate robot trajectories that enable...

0 Vignesh Prasad, et al. ∙

research

∙ 10/14/2022

Hierarchical Policy Blending as Inference for Reactive Robot Control

Motion generation in cluttered, dense, and dynamic environments is a cen...

2 Kay Hansel, et al. ∙

research

∙ 10/07/2022

Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes

Monte Carlo methods have become increasingly relevant for control of non...

0 Joe Watson, et al. ∙

research

∙ 10/05/2022

Placing by Touching: An empirical study on the importance of tactile sensing for precise object placing

Tactile sensors are promising tools for endowing robots with embodied in...

0 Luca Lach, et al. ∙

research

∙ 09/12/2022

Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning

Effective exploration is critical for reinforcement learning agents in e...

5 Bang You, et al. ∙

research

∙ 09/08/2022

SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion

Multi-objective optimization problems are ubiquitous in robotics, e.g., ...

1 Julen Urain, et al. ∙

research

∙ 07/04/2022

Controlling the Cascade: Kinematic Planning for N-ball Toss Juggling

Dynamic movements are ubiquitous in human motor behavior as they tend to...

0 Kai Ploeger, et al. ∙

research

∙ 06/01/2022

Active Inference for Robotic Manipulation

Robotic manipulation stands as a largely unsolved problem despite signif...

0 Tim Schneider, et al. ∙

research

∙ 04/11/2022

Learning Implicit Priors for Motion Optimization

In this paper, we focus on the problem of integrating Energy-based Model...

4 Alexander Lambert, et al. ∙

research

∙ 03/28/2022

Revisiting Model-based Value Expansion

Model-based value expansion methods promise to improve the quality of va...

0 Daniel Palenicek, et al. ∙

research

∙ 03/20/2022

Accelerating Integrated Task and Motion Planning with Neural Feasibility Checking

As robots play an increasingly important role in the industrial, the exp...

3 Lei Xu, et al. ∙

research

∙ 03/09/2022

Dimensionality Reduction and Prioritized Exploration for Policy Search

Black-box policy optimization is a class of reinforcement learning algor...

5 Marius Memmel, et al. ∙

research

∙ 03/09/2022

Regularized Deep Signed Distance Fields for Reactive Motion Generation

Autonomous robots should operate in real-world dynamic environments and ...

5 Puze Liu, et al. ∙

research

∙ 03/08/2022

Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery

Robot assembly discovery is a challenging problem that lives at the inte...

12 Niklas Funk, et al. ∙

research

∙ 03/08/2022

Robot Learning of Mobile Manipulation with Reachability Behavior Priors

Mobile Manipulation (MM) systems are ideal candidates for taking up the ...

5 Snehal Jauhri, et al. ∙

research

∙ 03/08/2022

A Hierarchical Approach to Active Pose Estimation

Creating mobile robots which are able to find and manipulate objects in ...

0 Jascha Hellwig, et al. ∙

research

∙ 03/08/2022

Residual Robot Learning for Object-Centric Probabilistic Movement Primitives

It is desirable for future robots to quickly learn new tasks and adapt l...

0 João Carvalho, et al. ∙

research

∙ 03/08/2022

An Analysis of Measure-Valued Derivatives for Policy Gradients

Reinforcement learning methods for robotics are increasingly successful ...

0 João Carvalho, et al. ∙

research

∙ 03/07/2022

PAC-Bayesian Lifelong Learning For Multi-Armed Bandits

We present a PAC-Bayesian analysis of lifelong learning. In the lifelong...

0 Hamish Flynn, et al. ∙

research

∙ 03/03/2022

An Adaptive Human Driver Model for Realistic Race Car Simulations

Engineering a high-performance race car requires a direct consideration ...

0 Stefan Löckel, et al. ∙

research

∙ 03/02/2022

Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from Images

Recent methods for reinforcement learning from images use auxiliary task...

0 Bang You, et al. ∙

research

∙ 02/11/2022

A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search

Monte-Carlo Tree Search (MCTS) is a class of methods for solving complex...

0 Tuan Dam, et al. ∙

research

∙ 01/24/2022

Learning Geometric Constraints in Task and Motion Planning

Searching for bindings of geometric parameters in task and motion planni...

0 Tianyu Ren, et al. ∙

research

∙ 12/06/2021

Distilled Domain Randomization

Deep reinforcement learning is an effective tool to learn robot control ...

0 Julien Brosseit, et al. ∙

research

∙ 11/11/2021

Model-Based Reinforcement Learning for Stochastic Hybrid Systems

Optimal control of general nonlinear systems is a central challenge in a...

11 Hany Abdulsamad, et al. ∙

research

∙ 11/01/2021

Robot Learning from Randomized Simulations: A Review

The rise of deep learning has caused a paradigm shift in robotics resear...

0 Fabio Muratore, et al. ∙

research

∙ 10/24/2021

A Differentiable Newton-Euler Algorithm for Real-World Robotics

Obtaining dynamics models is essential for robotics to achieve accurate ...

0 Michael Lutter, et al. ∙

research

∙ 10/22/2021

Learning Stable Vector Fields on Lie Groups

Learning robot motions from demonstration requires having models that ar...

0 Julen Urain, et al. ∙

research

∙ 10/05/2021

Continuous-Time Fitted Value Iteration for Robust Policies

Solving the Hamilton-Jacobi-Bellman equation is important in many domain...

1 Michael Lutter, et al. ∙

research

∙ 10/05/2021

Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models

Deep learning has been widely used within learning algorithms for roboti...

0 Michael Lutter, et al. ∙

research

∙ 07/20/2021

An Empirical Analysis of Measure-Valued Derivatives for Policy Gradients

Reinforcement learning methods for robotics are increasingly successful ...

0 João Carvalho, et al. ∙

research

∙ 07/13/2021

Efficient and Reactive Planning for High Speed Robot Air Hockey

Highly dynamic robotic tasks require high-speed and reactive robots. The...

0 Puze Liu, et al. ∙

research

∙ 06/07/2021

High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

We introduce a method based on deep metric learning to perform Bayesian ...

37 Antoine Grosnit, et al. ∙

Jan Peters

Featured Co-authors