Connectionists: [Call for Paper] ICLR 2022 workshop on “Generalizable Policy Learning in the Physical World”

[Hao Su]

We are excited to announce the first workshop on “Generalizable Policy
Learning in the Physical World”
<https://ai-workshops.github.io/generalizable-policy-learning-in-the-physical-world/>
at ICLR 2022!

We are calling for papers. If you work on robotics, learning, vision, and
other relevant fields, please consider submitting. We accept both 4-page
short papers and 8-page long papers. The submission deadline is Feb 25, 2022.
Check our workshop website for more information:
https://ai-workshops.github.io/generalizable-policy-learning-in-the-physical-world/

Introduction to our workshop:

Generalization is particularly important when learning policies to interact
with the physical world. The spectrum of such policies is broad: the
policies can be high-level, such as action plans that concern temporal
dependencies and casualties of environment states; or low-level, such as
object manipulation skills to transform objects that are rigid,
articulated, soft, or even fluid. In the physical world, an embodied agent
can face a number of changing factors such as physical parameters, action
spaces, tasks, visual appearances of the scenes, geometry and topology of
the objects, etc. And many important real-world tasks involving
generalizable policy learning, e.g., visual navigation, object
manipulation, and autonomous driving. Therefore, learning generalizable
policies is crucial to developing intelligent embodied agents in the real
world.

Our main targeted participants are researchers interested in applying
learning methods to develop intelligent embodied agents in the physical
world. More specifically, target communities include, but are not limited
to: robotics, reinforcement learning, learning from demonstrations, offline
reinforcement learning, meta-learning, multi-task learning, 3D vision,
computer vision, computer graphics, and physical simulation.

In affiliation to this workshop, we are also organizing the ManiSkill
Challenge, which focuses on learning to manipulate unseen objects in
simulation with 3D visual inputs. We will announce winners and host winner
presentations in this workshop.



Have a wonderful day and stay safe throughout the pandemic!

Best,

Hao Su on behalf of the organizing committee

-- 
Hao Su
Assistant Professor
Department of Computer Science and Engineering
Jacobs School of Engineering
University of California, San Diego
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