no code implementations • 30 Mar 2024 • Arjun P S, Andrew Melnik, Gora Chand Nandi
We introduce a comprehensive framework capable of exploring an unfamiliar environment in search of an object by leveraging the capabilities of Large Language Models(LLMs) and Large Vision Language Models (LVLMs) in understanding the underlying semantics of our world.
no code implementations • 20 Mar 2024 • Yusuke Mikami, Andrew Melnik, Jun Miura, Ville Hautamäki
We demonstrate experimental results with LLMs that address robotics task planning problems.
no code implementations • 29 Jan 2024 • Federco Malato, Florian Leopold, Andrew Melnik, Ville Hautamaki
Behavioral cloning uses a dataset of demonstrations to learn a policy.
1 code implementation • 17 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.
no code implementations • 14 Dec 2023 • Andrew Melnik, Michael Büttner, Leon Harz, Lyon Brown, Gora Chand Nandi, Arjun PS, Gaurav Kumar Yadav, Rahul Kala, Robert Haschke
This report introduces our UniTeam agent - an improved baseline for the "HomeRobot: Open Vocabulary Mobile Manipulation" challenge.
no code implementations • 10 Dec 2023 • Jannik Sheikh, Andrew Melnik, Gora Chand Nandi, Robert Haschke
Reinforcement learning and Imitation Learning approaches utilize policy learning strategies that are difficult to generalize well with just a few examples of a task.
no code implementations • 15 Jun 2023 • Federico Malato, Florian Leopold, Ville Hautamaki, Andrew Melnik
Actions from a selected similar situation can be performed by the agent until representations of the agent's current situation and the selected experience diverge in the latent space.
no code implementations • 21 Apr 2023 • Krishan Rana, Andrew Melnik, Niko Sünderhauf
In this paper, we introduce a method for unifying language, action, and state information in a shared embedding space to facilitate a range of downstream tasks in robot learning.
1 code implementation • 5 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.
no code implementations • 23 Mar 2023 • Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas, Sander Schulhoff, Brandon Houghton, Sharada Mohanty, Byron Galbraith, Ke Chen, Yan Song, Tianze Zhou, Bingquan Yu, He Liu, Kai Guan, Yujing Hu, Tangjie Lv, Federico Malato, Florian Leopold, Amogh Raut, Ville Hautamäki, Andrew Melnik, Shu Ishida, João F. Henriques, Robert Klassert, Walter Laurito, Ellen Novoseller, Vinicius G. Goecks, Nicholas Waytowich, David Watkins, Josh Miller, Rohin Shah
To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 2022.
1 code implementation • 30 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.
no code implementations • 27 Dec 2022 • Federico Malato, Florian Leopold, Amogh Raut, Ville Hautamäki, Andrew Melnik
Our approach can effectively recover meaningful demonstration trajectories and show human-like behavior of an agent in the Minecraft environment.
no code implementations • 18 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.
no code implementations • 5 Jul 2022 • Shivansh Beohar, Andrew Melnik
The practical application of learning agents requires sample efficient and interpretable algorithms.
no code implementations • 4 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.
2 code implementations • 3 Apr 2022 • Andrew Melnik, Eren Akbulut, Jannik Sheikh, Kira Loos, Michael Buettner, Tobias Lenze
AI Blitz XIII Faces challenge hosted on www. aicrowd. com platform consisted of five problems: Sentiment Classification, Age Prediction, Mask Prediction, Face Recognition, and Face De-Blurring.
1 code implementation • 31 Mar 2022 • Christian Eichenberger, Moritz Neun, Henry Martin, Pedro Herruzo, Markus Spanring, Yichao Lu, Sungbin Choi, Vsevolod Konyakhin, Nina Lukashina, Aleksei Shpilman, Nina Wiedemann, Martin Raubal, Bo wang, Hai L. Vu, Reza Mohajerpoor, Chen Cai, Inhi Kim, Luca Hermes, Andrew Melnik, Riza Velioglu, Markus Vieth, Malte Schilling, Alabi Bojesomo, Hasan Al Marzouqi, Panos Liatsis, Jay Santokhi, Dylan Hillier, Yiming Yang, Joned Sarwar, Anna Jordan, Emil Hewage, David Jonietz, Fei Tang, Aleksandra Gruca, Michael Kopp, David Kreil, Sepp Hochreiter
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins.
1 code implementation • 11 Feb 2022 • Luca Hermes, Barbara Hammer, Andrew Melnik, Riza Velioglu, Markus Vieth, Malte Schilling
Accurate traffic prediction is a key ingredient to enable traffic management like rerouting cars to reduce road congestion or regulating traffic via dynamic speed limits to maintain a steady flow.
no code implementations • 16 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.
1 code implementation • 20 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.
no code implementations • 7 Jun 2021 • William Hebgen Guss, Stephanie Milani, Nicholay Topin, Brandon Houghton, Sharada Mohanty, Andrew Melnik, Augustin Harter, Benoit Buschmaas, Bjarne Jaster, Christoph Berganski, Dennis Heitkamp, Marko Henning, Helge Ritter, Chengjie WU, Xiaotian Hao, Yiming Lu, Hangyu Mao, Yihuan Mao, Chao Wang, Michal Opanowicz, Anssi Kanervisto, Yanick Schraner, Christian Scheller, Xiren Zhou, Lu Liu, Daichi Nishio, Toi Tsuneda, Karolis Ramanauskas, Gabija Juceviciute
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field.
2 code implementations • 14 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.
no code implementations • 27 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.
2 code implementations • 2 Apr 2018 • Łukasz Kidziński, Sharada Prasanna Mohanty, Carmichael Ong, Zhewei Huang, Shuchang Zhou, Anton Pechenko, Adam Stelmaszczyk, Piotr Jarosik, Mikhail Pavlov, Sergey Kolesnikov, Sergey Plis, Zhibo Chen, Zhizheng Zhang, Jiale Chen, Jun Shi, Zhuobin Zheng, Chun Yuan, Zhihui Lin, Henryk Michalewski, Piotr Miłoś, Błażej Osiński, Andrew Melnik, Malte Schilling, Helge Ritter, Sean Carroll, Jennifer Hicks, Sergey Levine, Marcel Salathé, Scott Delp
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course.