Search Results for author: Helge Ritter

Found 16 papers, 6 papers with code

Benchmarks for Physical Reasoning AI

1 code implementation17 Dec 2023 Andrew Melnik, Robin Schiewer, Moritz Lange, Andrei Muresanu, Mozhgan Saeidi, Animesh Garg, Helge Ritter

Therefore, we aim to offer an overview of existing benchmarks and their solution approaches and propose a unified perspective for measuring the physical reasoning capacity of AI systems.

Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot

no code implementations13 Nov 2023 Luca Lach, Robert Haschke, Davide Tateo, Jan Peters, Helge Ritter, Júlia Borràs, Carme Torras

The advent of tactile sensors in robotics has sparked many ideas on how robots can leverage direct contact measurements of their environment interactions to improve manipulation tasks.

Inductive Bias

Shape complexity estimation using VAE

1 code implementation5 Apr 2023 Markus Rothgaenger, Andrew Melnik, Helge Ritter

In this paper, we compare methods for estimating the complexity of two-dimensional shapes and introduce a method that exploits reconstruction loss of Variational Autoencoders with different sizes of latent vectors.

Attribute

Stroke-based Rendering: From Heuristics to Deep Learning

1 code implementation30 Dec 2022 Florian Nolte, Andrew Melnik, Helge Ritter

In the last few years, artistic image-making with deep learning models has gained a considerable amount of traction.

Neural Rendering Vector Graphics

Face Generation and Editing with StyleGAN: A Survey

no code implementations18 Dec 2022 Andrew Melnik, Maksim Miasayedzenkau, Dzianis Makarovets, Dzianis Pirshtuk, Eren Akbulut, Dennis Holzmann, Tarek Renusch, Gustav Reichert, Helge Ritter

Our goal with this survey is to provide an overview of the state of the art deep learning methods for face generation and editing using StyleGAN.

Face Generation Face Swapping +1

Solving Learn-to-Race Autonomous Racing Challenge by Planning in Latent Space

no code implementations4 Jul 2022 Shivansh Beohar, Fabian Heinrich, Rahul Kala, Helge Ritter, Andrew Melnik

The agent is required to pass the previously unknown F1-style track in the minimum time with the least amount of off-road driving violations.

Road Segmentation

YOLO -- You only look 10647 times

no code implementations16 Jan 2022 Christian Limberg, Andrew Melnik, Augustin Harter, Helge Ritter

With this work we are explaining the "You Only Look Once" (YOLO) single-stage object detection approach as a parallel classification of 10647 fixed region proposals.

Classification Image Classification +4

Critic Guided Segmentation of Rewarding Objects in First-Person Views

1 code implementation20 Jul 2021 Andrew Melnik, Augustin Harter, Christian Limberg, Krishan Rana, Niko Suenderhauf, Helge Ritter

This work discusses a learning approach to mask rewarding objects in images using sparse reward signals from an imitation learning dataset.

Imitation Learning

Optimizing piano practice with a utility-based scaffold

no code implementations21 Jun 2021 Alexandra Moringen, Sören Rüttgers, Luisa Zintgraf, Jason Friedman, Helge Ritter

Ideally, a focus on a particular practice method should be made in a way to maximize the learner's progress in learning to play the piano.

Guiding Representation Learning in Deep Generative Models with Policy Gradients

no code implementations1 Jan 2021 Luca Lach, Timo Korthals, Malte Schilling, Helge Ritter

Therefore, this paper investigates the issues of joint training approaches and explores incorporation of policy gradients from RL into the VAE's latent space to find a task-specific latent space representation.

Reinforcement Learning (RL) Representation Learning

Solving Physics Puzzles by Reasoning about Paths

2 code implementations14 Nov 2020 Augustin Harter, Andrew Melnik, Gaurav Kumar, Dhruv Agarwal, Animesh Garg, Helge Ritter

We propose a new deep learning model for goal-driven tasks that require intuitive physical reasoning and intervention in the scene to achieve a desired end goal.

Object

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility

no code implementations13 Aug 2019 Malte Schilling, Helge Ritter, Frank W. Ohl

Recent developments in machine-learning algorithms have led to impressive performance increases in many traditional application scenarios of artificial intelligence research.

BIG-bench Machine Learning Incremental Learning +2

Learning efficient haptic shape exploration with a rigid tactile sensor array

no code implementations20 Feb 2019 Sascha Fleer, Alexandra Moringen, Roberta L. Klatzky, Helge Ritter

In the present work, we connect recent advances in recurrent models of visual attention with previous insights about the organisation of human haptic search behavior, exploratory procedures and haptic glances for a novel architecture that learns a generative model of haptic exploration in a simulated three-dimensional environment.

Modularization of End-to-End Learning: Case Study in Arcade Games

no code implementations27 Jan 2019 Andrew Melnik, Sascha Fleer, Malte Schilling, Helge Ritter

Complex environments and tasks pose a difficult problem for holistic end-to-end learning approaches.

Atari Games

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