382 citations · 1.2k across the 106 of their papers we have counts for
123 papers
Accelerating Motion Planning via Optimal Transport
An T. Le, Georgia Chalvatzaki, Armin Biess +1
Motion planning is still an open problem for many disciplines, e.g., robotics, autonomous driving, due to their need for high computational resources that hinder real-time, efficie…
Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures
Hamish Flynn, David Reeb, Melih Kandemir +1
We present improved algorithms with worst-case regret guarantees for the stochastic linear bandit problem. The widely used "optimism in the face of uncertainty" principle reduces a…
Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning
Pascal Klink, Florian Wolf, Kai Ploeger +2
Reinforcement Learning (RL) allows learning non-trivial robot control laws purely from data. However, many successful applications of RL have relied on ad-hoc regularizations, such…
Sampling-Free Probabilistic Deep State-Space Models
Andreas Look, Melih Kandemir, Barbara Rakitsch +1
Many real-world dynamical systems can be described as State-Space Models (SSMs). In this formulation, each observation is emitted by a latent state, which follows first-order Marko…
On the Benefit of Optimal Transport for Curriculum Reinforcement Learning
Pascal Klink, Carlo D'Eramo, Jan Peters +1
Curriculum reinforcement learning (CRL) allows solving complex tasks by generating a tailored sequence of learning tasks, starting from easy ones and subsequently increasing their…
Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles
Johanna Bethge, Maik Pfefferkorn, Alexander Rose +2
We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes for predicting human driving behavior. The proposed approach employs…
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors
Shangding Gu, Alap Kshirsagar, Yali Du +3
Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real world requires ensuring the safety of the robot and its environment. Safe Robot RL (SRRL)…
Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks
Piotr Kicki, Puze Liu, Davide Tateo +4
Motion planning is a mature area of research in robotics with many well-established methods based on optimization or sampling the state space, suitable for solving kinematic motion…
Hierarchical Policy Blending As Optimal Transport
An T. Le, Kay Hansel, Jan Peters +1
We present hierarchical policy blending as optimal transport (HiPBOT). HiPBOT hierarchically adjusts the weights of low-level reactive expert policies of different agents by adding…
How Crucial is Transformer in Decision Transformer?
Max Siebenborn, Boris Belousov, Junning Huang +1
Decision Transformer (DT) is a recently proposed architecture for Reinforcement Learning that frames the decision-making process as an auto-regressive sequence modeling problem and…
Active Exploration for Robotic Manipulation
Tim Schneider, Boris Belousov, Georgia Chalvatzaki +3
Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning in recent years. One of the key challenges in manipulation i…
MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction
Vignesh Prasad, Dorothea Koert, Ruth Stock-Homburg +2
Modeling interaction dynamics to generate robot trajectories that enable a robot to adapt and react to a human's actions and intentions is critical for efficient and effective coll…
Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes
Joe Watson, Jan Peters
Monte Carlo methods have become increasingly relevant for control of non-differentiable systems, approximate dynamics models and learning from data. These methods scale to high-dim…
Hierarchical Policy Blending as Inference for Reactive Robot Control
Kay Hansel, Julen Urain, Jan Peters +1
Motion generation in cluttered, dense, and dynamic environments is a central topic in robotics, rendered as a multi-objective decision-making problem. Current approaches trade-off…
Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction
Puze Liu, Kuo Zhang, Davide Tateo +4
Safety is a crucial property of every robotic platform: any control policy should always comply with actuator limits and avoid collisions with the environment and humans. In reinfo…
SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion
Julen Urain, Niklas Funk, Jan Peters +1
Multi-objective optimization problems are ubiquitous in robotics, e.g., the optimization of a robot manipulation task requires a joint consideration of grasp pose configurations, c…
Monte-Carlo Robot Path Planning
T. Dam, G. Chalvatzaki, J. Peters +1
Path planning is a crucial algorithmic approach for designing robot behaviors. Sampling-based approaches, like rapidly exploring random trees (RRTs) or probabilistic roadmaps, are…
Controlling the Cascade: Kinematic Planning for N-ball Toss Juggling
Kai Ploeger, Jan Peters
Dynamic movements are ubiquitous in human motor behavior as they tend to be more efficient and can solve a broader range of skill domains than their quasi-static counterparts. For…
Active Inference for Robotic Manipulation
Tim Schneider, Boris Belousov, Hany Abdulsamad +1
Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning in the last decades. One of the central challenges of manipu…
Accelerating Integrated Task and Motion Planning with Neural Feasibility Checking
Lei Xu, Tianyu Ren, Georgia Chalvatzaki +1
As robots play an increasingly important role in the industrial, the expectations about their applications for everyday living tasks are getting higher. Robots need to perform long…
A Hierarchical Approach to Active Pose Estimation
Jascha Hellwig, Mark Baierl, Joao Carvalho +2
Creating mobile robots which are able to find and manipulate objects in large environments is an active topic of research. These robots not only need to be capable of searching for…
Residual Robot Learning for Object-Centric Probabilistic Movement Primitives
Joao Carvalho, Dorothea Koert, Marek Daniv +1
It is desirable for future robots to quickly learn new tasks and adapt learned skills to constantly changing environments. To this end, Probabilistic Movement Primitives (ProMPs) h…
Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from Images
Bang You, Oleg Arenz, Youping Chen +1
Recent methods for reinforcement learning from images use auxiliary tasks to learn image features that are used by the agent's policy or Q-function. In particular, methods based on…
Regularized Deep Signed Distance Fields for Reactive Motion Generation
Puze Liu, Kuo Zhang, Davide Tateo +3
Autonomous robots should operate in real-world dynamic environments and collaborate with humans in tight spaces. A key component for allowing robots to leave structured lab and man…