Search Results for author: Tamim Asfour

Found 16 papers, 10 papers with code

Graph-based Task-specific Prediction Models for Interactions between Deformable and Rigid Objects

1 code implementation4 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.


Learning to Shift Attention for Motion Generation

1 code implementation24 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.

Density Estimation

Object and Relation Centric Representations for Push Effect Prediction

no code implementations3 Feb 2021 Ahmet E. Tekden, Aykut Erdem, Erkut Erdem, Tamim Asfour, Emre Ugur

For this reason, effect prediction and parameter estimation with pushing actions have been heavily investigated in the literature.

Uncertainty-aware Contact-safe Model-based Reinforcement Learning

no code implementations16 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

Learning Compliance Adaptation in Contact-Rich Manipulation

no code implementations1 May 2020 Jianfeng Gao, You Zhou, Tamim Asfour

Compliant robot behavior is crucial for the realization of contact-rich manipulation tasks.

Anomaly Detection

Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases

1 code implementation9 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.

Noise Regularization for Conditional Density Estimation

1 code implementation21 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.

Density Estimation

Model-Based Reinforcement Learning via Meta-Policy Optimization

no code implementations14 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

Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets

2 code implementations2 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.

Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution

1 code implementation12 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.

Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks

1 code implementation18 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.

Feature Engineering Machine Translation

The KIT Motion-Language Dataset

1 code implementation13 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.

Motion Capture

Resource-Aware Programming for Robotic Vision

no code implementations12 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.

Data-Driven Grasp Synthesis - A Survey

no code implementations10 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.


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