Marc Toussaint

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University of Stuttgart ∙ Ruhr-Universität Bochum

Professor in Machine Learning and Robotics at University of Stuttgart

MoGaze: A Dataset of Full-Body Motions that Includes Workspace Geometry and Eye-Gaze

As robots become more present in open human environments, it will become...
11/23/2020 ∙ by Philipp Kratzer, et al. ∙ 0 ∙ share

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Learning Efficient Constraint Graph Sampling for Robotic Sequential Manipulation

Efficient sampling from constraint manifolds, and thereby generating a d...
11/09/2020 ∙ by Joaquim Ortiz de Haro, et al. ∙ 0 ∙ share

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Efficient Sampling of Transition Constraints for Motion Planning under Sliding Contacts

Contact-based motion planning for manipulation, object exploration or ba...
11/03/2020 ∙ by Marie-Therese Khoury, et al. ∙ 0 ∙ share

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Sparse Multilevel Roadmaps on Fiber Bundles for High-Dimensional Motion Planning

Sparse roadmaps are important to compactly represent state spaces, to de...
11/02/2020 ∙ by Andreas Orthey, et al. ∙ 0 ∙ share

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Section Patterns: Efficiently Solving Narrow Passage Problems using Multilevel Motion Planning

Sampling-based planning methods often become inefficient due to narrow p...
10/27/2020 ∙ by Andreas Orthey, et al. ∙ 0 ∙ share

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Natural Gradient Shared Control

We propose a formalism for shared control, which is the problem of defin...
07/30/2020 ∙ by Yoojin Oh, et al. ∙ 0 ∙ share

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Anticipating Human Intention for Full-Body Motion Prediction in Object Grasping and Placing Tasks

Motion prediction in unstructured environments is a difficult problem an...
07/20/2020 ∙ by Philipp Kratzer, et al. ∙ 0 ∙ share

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Multilevel Motion Planning: A Fiber Bundle Formulation

Motion planning problems involving high-dimensional state spaces can oft...
07/18/2020 ∙ by Andreas Orthey, et al. ∙ 0 ∙ share

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Qgraph-bounded Q-learning: Stabilizing Model-Free Off-Policy Deep Reinforcement Learning

In state of the art model-free off-policy deep reinforcement learning, a...
07/15/2020 ∙ by Sabrina Hoppe, et al. ∙ 13 ∙ share

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An Interior Point Method Solving Motion Planning Problems with Narrow Passages

Algorithmic solutions for the motion planning problem have been investig...
07/09/2020 ∙ by Jim Mainprice, et al. ∙ 0 ∙ share

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Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image

In this paper, we propose a deep convolutional recurrent neural network ...
06/09/2020 ∙ by Danny Driess, et al. ∙ 2 ∙ share

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Optimized Directed Roadmap Graph for Multi-Agent Path Finding Using Stochastic Gradient Descent

We present a novel approach called Optimized Directed Roadmap Graph (ODR...
03/29/2020 ∙ by Christian Henkel, et al. ∙ 2 ∙ share

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Robust Task and Motion Planning for Long-Horizon Architectural Construction Planning

Integrating robotic systems in architectural and construction processes ...
03/17/2020 ∙ by Valentin N. Hartmann, et al. ∙ 0 ∙ share

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Probabilistic Framework for Constrained Manipulations and Task and Motion Planning under Uncertainty

Logic-Geometric Programming (LGP) is a powerful motion and manipulation ...
03/09/2020 ∙ by Jung-Su Ha, et al. ∙ 0 ∙ share

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Describing Physics For Physical Reasoning: Force-based Sequential Manipulation Planning

Physical reasoning is a core aspect of intelligence in animals and human...
02/28/2020 ∙ by Marc Toussaint, et al. ∙ 0 ∙ share

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Visualizing Local Minima in Multi-Robot Motion Planning using Morse Theory

Multi-robot motion planning problems often have many local minima. It is...
02/11/2020 ∙ by Andreas Orthey, et al. ∙ 0 ∙ share

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Prediction of Human Full-Body Movements with Motion Optimization and Recurrent Neural Networks

Human movement prediction is difficult as humans naturally exhibit compl...
10/04/2019 ∙ by Philipp Kratzer, et al. ∙ 25 ∙ share

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Motion Planning Explorer: Visualizing Local Minima using a Local-Minima Tree

We present an algorithm to visualize local minima in a motion planning p...
09/11/2019 ∙ by Andreas Orthey, et al. ∙ 4 ∙ share

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Deep Workpiece Region Segmentation for Bin Picking

For most industrial bin picking solutions, the pose of a workpiece is lo...
09/08/2019 ∙ by Muhammad Usman Khalid, et al. ∙ 8 ∙ share

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An Optimal Algorithm to Solve the Combined Task Allocation and Path Finding Problem

We consider multi-agent transport task problems where, e.g. in a factory...
07/24/2019 ∙ by Christian Henkel, et al. ∙ 0 ∙ share

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Learning Arbitration for Shared Autonomy by Hindsight Data Aggregation

In this paper we present a framework for the teleoperation of pick-and-p...
06/28/2019 ∙ by Yoojin Oh, et al. ∙ 0 ∙ share

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Motion Prediction with Recurrent Neural Network Dynamical Models and Trajectory Optimization

Predicting human motion in unstructured and dynamic environments is diff...
06/28/2019 ∙ by Philipp Kratzer, et al. ∙ 0 ∙ share

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Rapidly-Exploring Quotient-Space Trees: Motion Planning using Sequential Simplifications

Motion planning problems can be simplified by admissible projections of ...
06/04/2019 ∙ by Andreas Orthey, et al. ∙ 8 ∙ share

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Trajectory-Based Off-Policy Deep Reinforcement Learning

Policy gradient methods are powerful reinforcement learning algorithms a...
05/14/2019 ∙ by Andreas Doerr, et al. ∙ 5 ∙ share

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Kinematic Morphing Networks for Manipulation Skill Transfer

The transfer of a robot skill between different geometric environments i...
03/05/2018 ∙ by Peter Englert, et al. ∙ 0 ∙ share

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Probabilistic Recurrent State-Space Models

State-space models (SSMs) are a highly expressive model class for learni...
01/31/2018 ∙ by Andreas Doerr, et al. ∙ 0 ∙ share

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Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints

In this paper, we present a new task that investigates how people intera...
07/25/2017 ∙ by Ilker Yildirim, et al. ∙ 0 ∙ share

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Identification of Unmodeled Objects from Symbolic Descriptions

Successful human-robot cooperation hinges on each agent's ability to pro...
01/23/2017 ∙ by Andrea Baisero, et al. ∙ 0 ∙ share

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Advancing Bayesian Optimization: The Mixed-Global-Local (MGL) Kernel and Length-Scale Cool Down

Bayesian Optimization (BO) has become a core method for solving expensiv...
12/09/2016 ∙ by Kim Peter Wabersich, et al. ∙ 0 ∙ share

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The Advantage of Cross Entropy over Entropy in Iterative Information Gathering

Gathering the most information by picking the least amount of data is a ...
09/26/2014 ∙ by Johannes Kulick, et al. ∙ 0 ∙ share

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Planning with Noisy Probabilistic Relational Rules

Noisy probabilistic relational rules are a promising world model represe...
01/16/2014 ∙ by Tobias Lang, et al. ∙ 0 ∙ share

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Path Integral Control by Reproducing Kernel Hilbert Space Embedding

We present an embedding of stochastic optimal control problems, of the s...
08/13/2012 ∙ by Konrad Rawlik, et al. ∙ 0 ∙ share

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Hierarchical POMDP Controller Optimization by Likelihood Maximization

Planning can often be simpli ed by decomposing the task into smaller tas...
06/13/2012 ∙ by Marc Toussaint, et al. ∙ 0 ∙ share

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Approximate Inference and Stochastic Optimal Control

We propose a novel reformulation of the stochastic optimal control probl...
09/20/2010 ∙ by Konrad Rawlik, et al. ∙ 0 ∙ share

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Notes on information geometry and evolutionary processes

In order to analyze and extract different structural properties of distr...
08/20/2004 ∙ by Marc Toussaint, et al. ∙ 0 ∙ share

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Recent Results on No-Free-Lunch Theorems for Optimization

The sharpened No-Free-Lunch-theorem (NFL-theorem) states that the perfor...
03/31/2003 ∙ by Christian Igel, et al. ∙ 0 ∙ share

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The structure of evolutionary exploration: On crossover, buildings blocks and Estimation-Of-Distribution Algorithms

The notion of building blocks can be related to the structure of the off...
12/11/2002 ∙ by Marc Toussaint, et al. ∙ 0 ∙ share

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Neutrality: A Necessity for Self-Adaptation

Self-adaptation is used in all main paradigms of evolutionary computatio...
04/16/2002 ∙ by Marc Toussaint, et al. ∙ 0 ∙ share

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A neural model for multi-expert architectures

We present a generalization of conventional artificial neural networks t...
02/19/2002 ∙ by Marc Toussaint, et al. ∙ 0 ∙ share

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On model selection and the disability of neural networks to decompose tasks

A neural network with fixed topology can be regarded as a parametrizatio...
02/19/2002 ∙ by Marc Toussaint, et al. ∙ 0 ∙ share

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On Classes of Functions for which No Free Lunch Results Hold

In a recent paper it was shown that No Free Lunch results hold for any s...
08/21/2001 ∙ by Christian Igel, et al. ∙ 0 ∙ share

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Self-adaptive exploration in evolutionary search

We address a primary question of computational as well as biological res...
02/05/2001 ∙ by Marc Toussaint, et al. ∙ 0 ∙ share

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Marc Toussaint

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