Search Results for author: Marcin Andrychowicz

Found 20 papers, 8 papers with code

What Matters for Adversarial Imitation Learning?

no code implementations NeurIPS 2021 Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz

To tackle this issue, we implement more than 50 of these choices in a generic adversarial imitation learning framework and investigate their impacts in a large-scale study (>500k trained agents) with both synthetic and human-generated demonstrations.

Continuous Control Imitation Learning

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.

Friction reinforcement-learning +1

Sim-to-Real Transfer of Robotic Control with Dynamics Randomization

no code implementations18 Oct 2017 Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, Pieter Abbeel

By randomizing the dynamics of the simulator during training, we are able to develop policies that are capable of adapting to very different dynamics, including ones that differ significantly from the dynamics on which the policies were trained.

Robotics Systems and Control

Asymmetric Actor Critic for Image-Based Robot Learning

no code implementations18 Oct 2017 Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, Pieter Abbeel

While several recent works have shown promising results in transferring policies trained in simulation to the real world, they often do not fully utilize the advantage of working with a simulator.

Decision Making Reinforcement Learning (RL)

Domain Randomization and Generative Models for Robotic Grasping

no code implementations17 Oct 2017 Joshua Tobin, Lukas Biewald, Rocky Duan, Marcin Andrychowicz, Ankur Handa, Vikash Kumar, Bob McGrew, Jonas Schneider, Peter Welinder, Wojciech Zaremba, Pieter Abbeel

In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to object synthesis.

Robotic Grasping

One-Shot Imitation Learning

no code implementations NeurIPS 2017 Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba

A neural net is trained that takes as input one demonstration and the current state (which initially is the initial state of the other demonstration of the pair), and outputs an action with the goal that the resulting sequence of states and actions matches as closely as possible with the second demonstration.

Feature Engineering Imitation Learning +1

Learning Efficient Algorithms with Hierarchical Attentive Memory

no code implementations9 Feb 2016 Marcin Andrychowicz, Karol Kurach

In this paper, we propose and investigate a novel memory architecture for neural networks called Hierarchical Attentive Memory (HAM).

Neural Random-Access Machines

no code implementations19 Nov 2015 Karol Kurach, Marcin Andrychowicz, Ilya Sutskever

In this paper, we propose and investigate a new neural network architecture called Neural Random Access Machine.

Modeling Bitcoin Contracts by Timed Automata

no code implementations8 May 2014 Marcin Andrychowicz, Stefan Dziembowski, Daniel Malinowski, Łukasz Mazurek

We hope that our work can draw the attention of the researchers working on formal modeling to the problem of the Bitcoin contract verification, and spark off more research on this topic.

Cryptography and Security

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