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Saran Tunyasuvunakool

5 accepted papers

2021

Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion

Michael Bloesch, Jan Humplik, Viorica Patraucean, Roland Hafner, Tuomas Haarnoja, Arunkumar Byravan +9
CoRL 2021poster

Algorithms for self-learning systems have made considerable progress in recent years, yet safety concerns and the need for additional instrumentation have so far largely limited learning experiments with real robots to well controlled lab settings. In this paper, we demonstrate how a small bipedal r…

Cited by 24Source ↗Scholar ↗
2020

Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion

Roland Hafner, Tim Hertweck, Philipp Kloeppner, Michael Bloesch, Michael Neunert, Markus Wulfmeier +3
CoRL 2020

Modern Reinforcement Learning (RL) algorithms promise to solve difficult motor control problems directly from raw sensory inputs. Their attraction is due in part to the fact that they can represent a general class of methods that allow to learn a solution with a reasonably set reward and minimal pri

Cited by 0Source ↗PDF ↗Scholar ↗
2019

Emergent Coordination Through Competition

Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel
ICLR 2019poster

We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics. We demonstrate that decentralized, population-based training with co-play can lead to a progression in agents' b…

Cited by 186Source ↗PDF ↗Scholar ↗
2019

Hierarchical Visuomotor Control of Humanoids

Josh Merel, Arun Ahuja, Vu Pham, Saran Tunyasuvunakool, Siqi Liu, Dhruva Tirumala +2
ICLR 2019poster

We aim to build complex humanoid agents that integrate perception, motor control, and memory. In this work, we partly factor this problem into low-level motor control from proprioception and high-level coordination of the low-level skills informed by vision. We develop an architecture capable of sur…

Cited by 124Source ↗PDF ↗Scholar ↗
2018

Reinforcement and Imitation Learning for Diverse Visuomotor Skills

Yuke Zhu, Ziyu Wang, Josh Merel, Andrei Rusu, Tom Erez, Serkan Cabi +5
RSS 2018poster

We propose a general model-free deep reinforcement learning method and apply it to robotic manipulation tasks. Our approach leverages a small amount of demonstration data to assist a reinforcement learning agent. We train end-to-end visuomotor policies to learn a direct mapping from RGB camera input…

Cited by 398Source ↗PDF ↗Scholar ↗