no code implementations • 28 Oct 2024 • Luis Augenstein, Noémie Jaquier, Tamim Asfour, Leonel Rozo
Probabilistic Latent Variable Models (LVMs) have proven effective in capturing complex, high-dimensional data through lower-dimensional representations.
no code implementations • 24 Oct 2024 • Katharina Friedl, Noémie Jaquier, Jens Lundell, Tamim Asfour, Danica Kragic
By incorporating physical consistency as inductive bias, deep neural networks display increased generalization capabilities and data efficiency in learning nonlinear dynamic models.
no code implementations • 26 Sep 2024 • Leonard Bärmann, Chad DeChant, Joana Plewnia, Fabian Peller-Konrad, Daniel Bauer, Tamim Asfour, Alex Waibel
Verbalization of robot experience, i. e., summarization of and question answering about a robot's past, is a crucial ability for improving human-robot interaction.
1 code implementation • 29 Jun 2024 • Markus Grotz, Mohit Shridhar, Tamim Asfour, Dieter Fox
To kickstart the benchmark, we extended several state-of-the art methods to bimanual manipulation and also present a language-conditioned behavioral cloning agent -- PerAct2, which enables the learning and execution of bimanual 6-DoF manipulation tasks.
1 code implementation • 14 Jun 2024 • Tu Anh Dinh, Carlos Mullov, Leonard Bärmann, Zhaolin Li, Danni Liu, Simon Reiß, Jueun Lee, Nathan Lerzer, Fabian Ternava, Jianfeng Gao, Tobias Röddiger, Alexander Waibel, Tamim Asfour, Michael Beigl, Rainer Stiefelhagen, Carsten Dachsbacher, Klemens Böhm, Jan Niehues
We evaluate the performance of various state-of-the-art LLMs on our new benchmark.
no code implementations • 15 Mar 2024 • Max Braun, Noémie Jaquier, Leonel Rozo, Tamim Asfour
We introduce Riemannian Flow Matching Policies (RFMP), a novel model for learning and synthesizing robot visuomotor policies.
no code implementations • 16 Feb 2024 • Timo Birr, Christoph Pohl, Abdelrahman Younes, Tamim Asfour
AutoGPT+P leverages this representation to derive and execute a plan for a task specified by the user in natural language.
no code implementations • 14 Dec 2023 • Niklas Hemken, Florian Jacob, Fabian Peller-Konrad, Rainer Kartmann, Tamim Asfour, Hannes Hartenstein
Humanoid robots will be able to assist humans in their daily life, in particular due to their versatile action capabilities.
no code implementations • 29 Nov 2023 • Noémie Jaquier, Michael C. Welle, Andrej Gams, Kunpeng Yao, Bernardo Fichera, Aude Billard, Aleš Ude, Tamim Asfour, Danica Kragic
Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents.
no code implementations • 11 Oct 2023 • Noémie Jaquier, Leonel Rozo, Tamim Asfour
In the realm of robotics, numerous downstream robotics tasks leverage machine learning methods for processing, modeling, or synthesizing data.
no code implementations • 8 Sep 2023 • Leonard Bärmann, Rainer Kartmann, Fabian Peller-Konrad, Jan Niehues, Alex Waibel, Tamim Asfour
In this paper, we propose a system to achieve incremental learning of complex behavior from natural interaction, and demonstrate its implementation on a humanoid robot.
no code implementations • 18 Oct 2022 • JongSeok Lee, Ribin Balachandran, Konstantin Kondak, Andre Coelho, Marco De Stefano, Matthias Humt, Jianxiang Feng, Tamim Asfour, Rudolph Triebel
This article presents a novel telepresence system for advancing aerial manipulation in dynamic and unstructured environments.
no code implementations • 4 Oct 2022 • Noémie Jaquier, Leonel Rozo, Miguel González-Duque, Viacheslav Borovitskiy, Tamim Asfour
This may be attributed to the lack of computational models that fill the gap between the discrete hierarchical structure of the taxonomy and the high-dimensional heterogeneous data associated to its categories.
1 code implementation • 22 Aug 2022 • Yahav Avigal, Lars Berscheid, Tamim Asfour, Torsten Kröger, Ken Goldberg
Folding garments reliably and efficiently is a long standing challenge in robotic manipulation due to the complex dynamics and high dimensional configuration space of garments.
no code implementations • 5 Jun 2022 • Fabian Peller-Konrad, Rainer Kartmann, Christian R. G. Dreher, Andre Meixner, Fabian Reister, Markus Grotz, Tamim Asfour
We introduce the memory system for our cognitive robot control architecture and its implementation in the robot software framework ArmarX.
1 code implementation • 2 Nov 2021 • Noémie Jaquier, Viacheslav Borovitskiy, Andrei Smolensky, Alexander Terenin, Tamim Asfour, Leonel Rozo
Bayesian optimization is a data-efficient technique which can be used for control parameter tuning, parametric policy adaptation, and structure design in robotics.
1 code implementation • 4 Mar 2021 • Zehang Weng, Fabian Paus, Anastasiia Varava, Hang Yin, Tamim Asfour, Danica Kragic
In an ablation study, we show the benefits of the two-stage model for single time step prediction and the effectiveness of the mixed-horizon model for long-term prediction tasks.
Robotics
1 code implementation • 24 Feb 2021 • You Zhou, Jianfeng Gao, Tamim Asfour
For multiple modes, we suggest to learn local latent representations of motion trajectories with a density estimation method based on real-valued non-volume preserving (RealNVP) transformations that provides a set of powerful, stably invertible, and learnable transformations.
no code implementations • 3 Feb 2021 • Ahmet E. Tekden, Aykut Erdem, Erkut Erdem, Tamim Asfour, Emre Ugur
Our approach enables the robot to predict and adapt the effect of a pushing action as it observes the scene.
no code implementations • 16 Oct 2020 • Cheng-Yu Kuo, Andreas Schaarschmidt, Yunduan Cui, Tamim Asfour, Takamitsu Matsubara
In typical MBRL, we cannot expect the data-driven model to generate accurate and reliable policies to the intended robotic tasks during the learning process due to sample scarcity.
Model-based Reinforcement Learning
reinforcement-learning
+2
no code implementations • 1 May 2020 • Jianfeng Gao, You Zhou, Tamim Asfour
Compliant robot behavior is crucial for the realization of contact-rich manipulation tasks.
1 code implementation • 9 Sep 2019 • Fabio Ferreira, Lin Shao, Tamim Asfour, Jeannette Bohg
The first, Graph Networks (GN) based approach, considers explicitly defined edge attributes and not only does it consistently underperform an auto-encoder baseline that we modified to predict future states, our results indicate how different edge attributes can significantly influence the predictions.
1 code implementation • 21 Jul 2019 • Jonas Rothfuss, Fabio Ferreira, Simon Boehm, Simon Walther, Maxim Ulrich, Tamim Asfour, Andreas Krause
To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training.
6 code implementations • ICLR 2019 • Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, Pieter Abbeel
Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood.
1 code implementation • 14 Sep 2018 • Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel
Finally, we demonstrate that our approach is able to match the asymptotic performance of model-free methods while requiring significantly less experience.
Model-based Reinforcement Learning
reinforcement-learning
+2
2 code implementations • 2 Jul 2018 • Fabio Ferreira, Jonas Rothfuss, Eren Erdal Aksoy, You Zhou, Tamim Asfour
We release two artificial datasets, Simulated Flying Shapes and Simulated Planar Manipulator that allow to test the learning ability of video processing systems.
1 code implementation • 12 Jan 2018 • Jonas Rothfuss, Fabio Ferreira, Eren Erdal Aksoy, You Zhou, Tamim Asfour
We present a novel deep neural network architecture for representing robot experiences in an episodic-like memory which facilitates encoding, recalling, and predicting action experiences.
10 code implementations • ICLR 2018 • Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Y. Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, Marcin Andrychowicz
Combining parameter noise with traditional RL methods allows to combine the best of both worlds.
no code implementations • 18 May 2017 • Matthias Plappert, Christian Mandery, Tamim Asfour
We evaluate our approach on 2, 846 human whole-body motions and 6, 187 natural language descriptions thereof from the KIT Motion-Language Dataset.
2 code implementations • 13 Jul 2016 • Matthias Plappert, Christian Mandery, Tamim Asfour
Linking human motion and natural language is of great interest for the generation of semantic representations of human activities as well as for the generation of robot activities based on natural language input.
no code implementations • 12 May 2014 • Johny Paul, Walter Stechele, Manfred Kröhnert, Tamim Asfour
The result indicate that the new programming model together with the extensions within the application layer, makes them highly adaptable; leading to better quality in the results obtained.
no code implementations • 10 Sep 2013 • Jeannette Bohg, Antonio Morales, Tamim Asfour, Danica Kragic
In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation.
Robotics