Search Results for author: Karol Arndt

Found 10 papers, 4 papers with code

Co-Imitation: Learning Design and Behaviour by Imitation

no code implementations2 Sep 2022 Chang Rajani, Karol Arndt, David Blanco-Mulero, Kevin Sebastian Luck, Ville Kyrki

To this end we propose a co-imitation methodology for adapting behaviour and morphology by matching state distributions of the demonstrator.

Imitation Learning

Online vs. Offline Adaptive Domain Randomization Benchmark

1 code implementation29 Jun 2022 Gabriele Tiboni, Karol Arndt, Giuseppe Averta, Ville Kyrki, Tatiana Tommasi

However, transferring the acquired knowledge to the real world can be challenging due to the reality gap.

Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models

no code implementations18 Apr 2022 Ali Ghadirzadeh, Petra Poklukar, Karol Arndt, Chelsea Finn, Ville Kyrki, Danica Kragic, Mårten Björkman

We present a data-efficient framework for solving sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable generative models.

Decision Making reinforcement-learning +2

SafeAPT: Safe Simulation-to-Real Robot Learning using Diverse Policies Learned in Simulation

1 code implementation27 Jan 2022 Rituraj Kaushik, Karol Arndt, Ville Kyrki

In this work, we introduce a novel learning algorithm called SafeAPT that leverages a diverse repertoire of policies evolved in the simulation and transfers the most promising safe policy to the real robot through episodic interaction.

DROPO: Sim-to-Real Transfer with Offline Domain Randomization

1 code implementation20 Jan 2022 Gabriele Tiboni, Karol Arndt, Ville Kyrki

In recent years, domain randomization over dynamics parameters has gained a lot of traction as a method for sim-to-real transfer of reinforcement learning policies in robotic manipulation; however, finding optimal randomization distributions can be difficult.

Reinforcement Learning (RL)

Affine Transport for Sim-to-Real Domain Adaptation

no code implementations25 May 2021 Anton Mallasto, Karol Arndt, Markus Heinonen, Samuel Kaski, Ville Kyrki

In this paper, we present affine transport -- a variant of optimal transport, which models the mapping between state transition distributions between the source and target domains with an affine transformation.

Domain Adaptation OpenAI Gym

Domain Curiosity: Learning Efficient Data Collection Strategies for Domain Adaptation

no code implementations12 Mar 2021 Karol Arndt, Oliver Struckmeier, Ville Kyrki

Domain adaptation is a common problem in robotics, with applications such as transferring policies from simulation to real world and lifelong learning.

Domain Adaptation

Few-shot model-based adaptation in noisy conditions

no code implementations16 Oct 2020 Karol Arndt, Ali Ghadirzadeh, Murtaza Hazara, Ville Kyrki

Few-shot adaptation is a challenging problem in the context of simulation-to-real transfer in robotics, requiring safe and informative data collection.

Reinforcement Learning (RL)

Meta Reinforcement Learning for Sim-to-real Domain Adaptation

no code implementations16 Sep 2019 Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh, Ville Kyrki

Modern reinforcement learning methods suffer from low sample efficiency and unsafe exploration, making it infeasible to train robotic policies entirely on real hardware.

Domain Adaptation Meta-Learning +3

Affordance Learning for End-to-End Visuomotor Robot Control

2 code implementations10 Mar 2019 Aleksi Hämäläinen, Karol Arndt, Ali Ghadirzadeh, Ville Kyrki

Training end-to-end deep robot policies requires a lot of domain-, task-, and hardware-specific data, which is often costly to provide.

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