Connectionists: [Jobs] M2 internship position in Robot Learning

[Nguyen, Sao Mai]

Dear all,

could you please share to anybody who might be interested in the following
internship position ?

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ENSTA, IP Paris is looking to hire a talented master student in machine
learning on a collaborative project with Ecole Polytechnique

Laboratory: U2IS, ENSTA Paris (http://u2is.ensta-paris.fr/) & LIX, Ecole
Polytechnique
The intern will be part of the laboratory U2IS of ENSTA Paris and will
collaborate with LIX, Ecole Polytechnique

Duration: 6 months, flexible dates

Contact : NGUYEN Sao Mai : nguyensmai at gmail.com

Context:
Fully autonomous robots have the potential to impact real-life
applications, like assisting elderly people. Autonomous robots must deal
with uncertain and continuously changing environments, where it is not
possible to program the robot tasks. Instead, the robot must continuously
learn new tasks and how to perform more complex tasks combining simpler
ones (i.e., a task hierarchy). This problem is called lifelong learning of
hierarchical tasks [5]. Hierarchical Reinforcement Learning (HRL) is a
recent approach for learning to solve long and complex tasks by decomposing
them into simpler subtasks. HRL could be regarded as an extension of the
standard Reinforcement Learning (RL) setting as it features high-level
agents selecting subtasks to perform and low-level agents learning actions
or policies to achieve them.

Summary:
This internship studies the applications of Hierarchical Reinforcement
Learning methods in robotics: Deploying autonomous robots in real world
environments typically introduces multiple difficulties among which is the
size of the observable space and the length of the required tasks.
Reinforcement Learning typically helps agents solve decision making
problems by autonomously discovering successful behaviours and learning
them. But these methods are known to struggle with long and complex tasks.
Hierarchical Reinforcement Learning extend this paradigm to decompose these
problems into easier subproblems with High-level agents determining which
subtasks need to be accomplished, and Low-level agent learning to achieve
them.
During this internship, the intern will :
• Get acquainted with the state of art in Hierarchical Reinforcement
Learning including the most notable algorithms [1, 2, 3], the challenges
they solve and their limitations.
• Reimplement some of these approaches and validate their results in
robotics simulated environments such as iGibson [4].
• Establish an experimental comparison of these methods with respect to
some research hypothesis.
The intern is expected to also collaborate with a PhD student whose work is
closely related to this topic.

References:
[1] Nachum, O.; Gu, S.; Lee, H.; and Levine, S. 2018. Data- Efficient
Hierarchical Reinforcement Learning. In Bengio, S.; Wallach, H. M.;
Larochelle, H.; Grauman, K.; Cesa- Bianchi, N.; and Garnett, R., eds.,
Advances in Neural Infor- mation Processing Systems 31: Annual Conference
on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8,
2018, Montre ́al, Canada, 3307–3317.
[2] Kulkarni, T. D.; Narasimhan, K.; Saeedi, A.; and Tenen- baum, J. 2016.
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction
and Intrinsic Motivation. In Lee, D.; Sugiyama, M.; Luxburg, U.; Guyon, I.;
and Garnett, R., eds., Advances in Neural Information Processing Systems,
volume 29. Curran Associates, Inc.
[3] Vezhnevets, A. S.; Osindero, S.; Schaul, T.; Heess, N.; Jaderberg, M.;
Silver, D.; and Kavukcuoglu, K. 2017. FeU- dal Networks for Hierarchical
Reinforcement Learning. CoRR, abs/1703.01161.
[4] Chengshu Li, Fei Xia, Roberto Mart ́ın-Mart ́ın, Michael Lingelbach,
Sanjana Srivastava, Bokui Shen, Kent Vainio, Cem Gokmen, Gokul Dharan,
Tanish Jain, Andrey Kurenkov, C. Karen Liu, Hyowon Gweon, Jiajun Wu, Li
Fei-Fei, and Silvio Savarese. igibson 2.0: Object-centric simulation for
robot learning of everyday household tasks, 2021. URL
https://arxiv.org/abs/2108.0327
[5] Nguyen, S. M., Duminy, N., Manoury, A., Duhaut, D., and Buche, C.
(2021). Robots Learn Increasingly Complex Tasks with Intrinsic Motivation
and Automatic Curriculum Learning. KI - Künstliche Intelligenz, 35(81-90).



Nguyen Sao Mai
nguyensmai at gmail.com
Researcher in Cognitive Developmental Robotics
<http://nguyensmai.free.fr>https://doi.org/10.1155/2022/5667223
http://nguyensmai.free.fr | Youtube <http://www.youtube.com/user/nguyensmai>
| Twitter <https://twitter.com/nguyensmai> | ResearchGate
<https://www.researchgate.net/profile/Sao_Mai_Nguyen> | Hal
<https://hal.inria.fr/search/index/?q=%2A&authIdHal_s=sao-mai-nguyen&sort=producedDate_tdate+desc>
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