Search Results for author: Matthias Plappert

Found 11 papers, 5 papers with code

Training Verifiers to Solve Math Word Problems

2 code implementations27 Oct 2021 Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, John Schulman

State-of-the-art language models can match human performance on many tasks, but they still struggle to robustly perform multi-step mathematical reasoning.

Mathematical Reasoning

Predicting Sim-to-Real Transfer with Probabilistic Dynamics Models

no code implementations27 Sep 2020 Lei M. Zhang, Matthias Plappert, Wojciech Zaremba

We further show that the transfer metric can predict the effect of training setups on policy transfer performance.

Learning Dexterous In-Hand Manipulation

no code implementations1 Aug 2018 OpenAI, Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, Wojciech Zaremba

We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand.

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

no code implementations18 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

no code implementations13 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

Classification of Human Whole-Body Motion using Hidden Markov Models

no code implementations5 May 2016 Matthias Plappert

These features are then used to perform the multi-label classification using two different approaches.

Classification General Classification +1

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