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Real Robot Challenge III - Learning Dexterous Manipulation from Offline Data in the Real World
Nico Gürtler · Georg Martius · Sebastian Blaes · Pavel Kolev · Cansu Sancaktar · Stefan Bauer · Manuel Wuethrich · Markus Wulfmeier · Martin Riedmiller · Arthur Allshire · Annika Buchholz · Bernhard Schölkopf

Tue Dec 06 03:00 AM -- 06:00 AM (PST) @ Virtual
Event URL: https://real-robot-challenge.com/ »

In this year's Real Robot Challenge, the participants will apply offline reinforcement learning (RL) to robotics datasets and evaluate their policies remotely on a cluster of real TriFinger robots. Usually, experimentation on real robots is quite costly and challenging. For this reason, a large part of the RL community uses simulators to develop and benchmark algorithms. However, insights gained in simulation do not necessarily translate to real robots, in particular for tasks involving complex interaction with the environment. The purpose of this competition is to alleviate this problem by allowing participants to experiment remotely with a real robot - as easily as in simulation. In the last two years, offline RL algorithms became increasingly popular and capable. This year’s Real Robot Challenge provides a platform for evaluation, comparison and showcasing the performance of these algorithms on real-world data. In particular, we propose a dexterous manipulation problem that involves pushing, grasping and in-hand orientation of blocks.

Author Information

Nico Gürtler (Max Planck Institute for Intelligent Systems, Tübingen)
Georg Martius (Max Planck Institute for Intelligent Systems)
Sebastian Blaes (Max-Planck Institute for Intelligent Systems, Tuebingen, Germany)
Pavel Kolev (Max Planck Institute for Intelligent Systems)
Cansu Sancaktar (Max Planck Institute for Intelligent Systems)
Stefan Bauer (Max Planck institute)
Manuel Wuethrich (MPI Intelligent Systems)
Markus Wulfmeier (DeepMind)
Martin Riedmiller (DeepMind)
Arthur Allshire (University of Toronto)
Annika Buchholz (Max Planck Institute for Intelligent Systems)
Bernhard Schölkopf (MPI for Intelligent Systems, Tübingen)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

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