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Albert Zhan*, Philip Zhao*, Lerrel Pinto, Pieter Abbeel, Michael Laskin
In Submission, 2020
A Framework for Efficient Robotic Manipulation (FERM) enables real robots to solve sparse-reward tasks from pixels in minutes.
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Michael Laskin*, Luke Metz*, Seth Nabarrao, Mark Saroufim, Badreddine Noune, Carlo Luschi, Jascha Sohl-Dickstein, Pieter Abbeel
In Submission, 2020
paper
/ twitter
Local learning, an alternative to global backpropagation, improves the efficiency of training deep nets in the high-compute regime.
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Adam Stooke, Kimin Lee, Pieter Abbeel, Michael Laskin
In Submission, 2020
paper
/ code
/ twitter
First algorithm that decouples unsupervised learning from reinforcement learning while matching or outperforming state-of-the-art end-to-end RL.
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Kimin Lee, Michael Laskin, Aravind Srinivas, Pieter Abbeel
In Submission, 2020
paper
/ code
Developed a unified framework for utilizing ensembles to greatly stabilize training of both state-based and pixel-based RL algorithms.
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Michael Laskin*, Kimin Lee*, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas
NeurIPS Spotlight (top 3% of submissions), 2020
paper
/ code
/ twitter
/ press
First extensive study of image augmentation in the RL setting. Showed that simple RL algorithms with augmented data achieve SOTA results on many common RL benchmarks.
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Michael Laskin*, Aravind Srinivas*, Pieter Abbeel
ICML, 2020
paper
/ code
/ twitter
Showed for the first time that RL from pixels can be as efficient as RL from state by leveraging unsupervised contrastive representations.
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Scott Emmons*, Ajay Jain*, Michael Laskin*, Thanard Kurutach, Pieter Abbeel, Deepak Pathak
NeurIPS, 2020
paper
/ video
/ code
Introduced novel state aggregation criterium - two-way consistency (TWC) - and utilized it to make any semi-parametric graphical method more robust. Proved theoretically that TWC enables near-optimal search.
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Michael Laskin, Thanard Kurutach, Pieter Abbeel
NeurIPS Deep Reinforcement Learning Workshop, 2019
paper
Discrete representations help reduce the size of the encoded observation space. We showed how utilizing a discrete bottleneck can improve goal-conditioned RL from pixels.
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Physics Research
During my physics PhD I studied many-body quantum
systems such as the Fractional Quantum Hall Effect, and discovered
a universal topological characteristic of such states.
Note: in theoretical physics alphabetical order is the usual convention for authorship.
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Emergent conformal symmetry and geometric transport properties of quantum Hall states on singular surfaces
T. Can, Y.H. Chiu, M. Laskin, P. Wiegmann
Physical review letters 117 (26), 266803, 2016
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Population of the giant pairing vibration
M. Laskin, R.F. Casten, A.O. Macchiavelli, R.M. Clark, D. Bucurescu
Physical Review C 93 (3), 034321, 2016
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Collective field theory for quantum Hall states
T. Can, M. Laskin, P. Wiegmann
Physical Review B 92 (23), 235141 , 2015
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Geometry of quantum Hall states: Gravitational anomaly and transport coefficients
T. Can, M. Laskin, P. Wiegmann
Annals of Physics 362, 752-794, 2015
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Fractional quantum Hall effect in a curved space: gravitational anomaly and electromagnetic response
T. Can, M. Laskin, P. Wiegmann
Physical review letters 113 (4), 046803, 2014
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Field Theory for Fractional Quantum Hall States
T. Can, M. Laskin, P. Wiegmann
arXiv preprint arXiv:1412.8716, 2014
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Some Aspects of the Giant-Pairing Vibration
A.O. Macchiavelli, R.M. Clark, M. Laskin, R.F. Casten
Bulletin of the American Physical Society, 2012
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