Marc Toussaint

research

∙ 12/13/2021

MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets

Recently, there has been a wealth of development in motion planning for ...

Constantinos Chamzas, et al. ∙

research

∙ 12/09/2021

Learning Neural Implicit Functions as Object Representations for Robotic Manipulation

Robotic manipulation planning is the problem of finding a sequence of ro...

Jung-Su Ha, et al. ∙

research

∙ 11/15/2021

Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics

Applications of Reinforcement Learning (RL) in robotics are often limite...

Ingmar Schubert, et al. ∙

research

∙ 10/28/2021

From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence

Machine learning has long since become a keystone technology, accelerati...

Nicholas Roy, et al. ∙

research

∙ 10/07/2021

RHH-LGP: Receding Horizon And Heuristics-Based Logic-Geometric Programming For Task And Motion Planning

Sequential decision-making and motion planning for robotic manipulation ...

Cornelius V. Braun, et al. ∙

research

∙ 10/02/2021

Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning

This work proposes an optimization-based manipulation planning framework...

Danny Driess, et al. ∙

research

∙ 08/24/2021

Learning to Arbitrate Human and Robot Control using Disagreement between Sub-Policies

In the context of teleoperation, arbitration refers to deciding how to b...

Yoojin Oh, et al. ∙

research

∙ 07/30/2021

Active Learning in Gaussian Process State Space Model

We investigate active learning in Gaussian Process state-space models (G...

Hon Sum Alec Yu, et al. ∙

research

∙ 07/14/2021

Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks

In high-dimensional state spaces, the usefulness of Reinforcement Learni...

Ingmar Schubert, et al. ∙

research

∙ 07/06/2021

Approximate Topological Optimization using Multi-Mode Estimation for Robot Motion Planning

In this extended abstract, we report on ongoing work towards an approxim...

Andreas Orthey, et al. ∙

research

∙ 07/05/2021

GraspME – Grasp Manifold Estimator

In this paper, we introduce a Grasp Manifold Estimator (GraspME) to dete...

Janik Hager, et al. ∙

research

∙ 07/05/2021

A System for Traded Control Teleoperation of Manipulation Tasks using Intent Prediction from Hand Gestures

This paper presents a teleoperation system that includes robot perceptio...

Yoojin Oh, et al. ∙

research

∙ 06/04/2021

Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

Robotic assembly planning has the potential to profoundly change how bui...

Valentin Noah Hartmann, et al. ∙

research

∙ 04/16/2021

Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks

In this paper, we tackle the problem of human-robot coordination in sequ...

An T. Le, et al. ∙

research

∙ 03/09/2021

Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping

Robotic manipulation of unknown objects is an important field of researc...

Marc Tuscher, et al. ∙

research

∙ 01/28/2021

Visualization of Nonlinear Programming for Robot Motion Planning

Nonlinear programming targets nonlinear optimization with constraints, w...

David Hägele, et al. ∙

research

∙ 11/23/2020

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...

Philipp Kratzer, et al. ∙

research

∙ 11/09/2020

Learning Efficient Constraint Graph Sampling for Robotic Sequential Manipulation

Efficient sampling from constraint manifolds, and thereby generating a d...

Joaquim Ortiz de Haro, et al. ∙

research

∙ 11/03/2020

Efficient Sampling of Transition Constraints for Motion Planning under Sliding Contacts

Contact-based motion planning for manipulation, object exploration or ba...

Marie-Therese Khoury, et al. ∙

research

∙ 11/02/2020

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

Sparse roadmaps are important to compactly represent state spaces, to de...

Andreas Orthey, et al. ∙

research

∙ 10/27/2020

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

Sampling-based planning methods often become inefficient due to narrow p...

Andreas Orthey, et al. ∙

research

∙ 07/30/2020

Natural Gradient Shared Control

We propose a formalism for shared control, which is the problem of defin...

Yoojin Oh, et al. ∙

research

∙ 07/20/2020

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

Motion prediction in unstructured environments is a difficult problem an...

Philipp Kratzer, et al. ∙

research

∙ 07/18/2020

Multilevel Motion Planning: A Fiber Bundle Formulation

Motion planning problems involving high-dimensional state spaces can oft...

Andreas Orthey, et al. ∙

research

∙ 07/15/2020

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...

Sabrina Hoppe, et al. ∙

research

∙ 07/09/2020

An Interior Point Method Solving Motion Planning Problems with Narrow Passages

Algorithmic solutions for the motion planning problem have been investig...

Jim Mainprice, et al. ∙

research

∙ 06/09/2020

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 ...

Danny Driess, et al. ∙

research

∙ 03/29/2020

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

We present a novel approach called Optimized Directed Roadmap Graph (ODR...

Christian Henkel, et al. ∙

research

∙ 03/17/2020

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

Integrating robotic systems in architectural and construction processes ...

Valentin N. Hartmann, et al. ∙

research

∙ 03/09/2020

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

Logic-Geometric Programming (LGP) is a powerful motion and manipulation ...

Jung-Su Ha, et al. ∙

research

∙ 02/28/2020

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

Physical reasoning is a core aspect of intelligence in animals and human...

Marc Toussaint, et al. ∙

research

∙ 02/11/2020

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

Multi-robot motion planning problems often have many local minima. It is...

Andreas Orthey, et al. ∙

research

∙ 10/04/2019

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

Human movement prediction is difficult as humans naturally exhibit compl...

Philipp Kratzer, et al. ∙

research

∙ 09/11/2019

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

We present an algorithm to visualize local minima in a motion planning p...

Andreas Orthey, et al. ∙

research

∙ 09/08/2019

Deep Workpiece Region Segmentation for Bin Picking

For most industrial bin picking solutions, the pose of a workpiece is lo...

Muhammad Usman Khalid, et al. ∙

research

∙ 07/24/2019

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...

Christian Henkel, et al. ∙

research

∙ 06/28/2019

Learning Arbitration for Shared Autonomy by Hindsight Data Aggregation

In this paper we present a framework for the teleoperation of pick-and-p...

Yoojin Oh, et al. ∙

research

∙ 06/28/2019

Motion Prediction with Recurrent Neural Network Dynamical Models and Trajectory Optimization

Predicting human motion in unstructured and dynamic environments is diff...

Philipp Kratzer, et al. ∙

research

∙ 06/04/2019

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

Motion planning problems can be simplified by admissible projections of ...

Andreas Orthey, et al. ∙

research

∙ 05/14/2019

Trajectory-Based Off-Policy Deep Reinforcement Learning

Policy gradient methods are powerful reinforcement learning algorithms a...

Andreas Doerr, et al. ∙

research

∙ 03/05/2018

Kinematic Morphing Networks for Manipulation Skill Transfer

The transfer of a robot skill between different geometric environments i...

Peter Englert, et al. ∙

research

∙ 01/31/2018

Probabilistic Recurrent State-Space Models

State-space models (SSMs) are a highly expressive model class for learni...

Andreas Doerr, et al. ∙

research

∙ 07/25/2017

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

In this paper, we present a new task that investigates how people intera...

Ilker Yildirim, et al. ∙

research

∙ 01/23/2017

Identification of Unmodeled Objects from Symbolic Descriptions

Successful human-robot cooperation hinges on each agent's ability to pro...

Andrea Baisero, et al. ∙

research

∙ 12/09/2016

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...

Kim Peter Wabersich, et al. ∙

research

∙ 09/26/2014

The Advantage of Cross Entropy over Entropy in Iterative Information Gathering

Gathering the most information by picking the least amount of data is a ...

Johannes Kulick, et al. ∙

research

∙ 01/16/2014

Planning with Noisy Probabilistic Relational Rules

Noisy probabilistic relational rules are a promising world model represe...

Tobias Lang, et al. ∙

research

∙ 08/13/2012

Path Integral Control by Reproducing Kernel Hilbert Space Embedding

We present an embedding of stochastic optimal control problems, of the s...

Konrad Rawlik, et al. ∙

research

∙ 06/13/2012

Hierarchical POMDP Controller Optimization by Likelihood Maximization

Planning can often be simpli ed by decomposing the task into smaller tas...

Marc Toussaint, et al. ∙

research

∙ 09/20/2010

Approximate Inference and Stochastic Optimal Control

We propose a novel reformulation of the stochastic optimal control probl...

Konrad Rawlik, et al. ∙

Marc Toussaint

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