Generalizable Policy Learning in the Physical World

April 29, ICLR 2022 Workshop (Virtual)

Introduction

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 causalities 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.

Learning generalizable policies in the physical world requires deep synergistic efforts across fields of vision, learning, and robotics, and poses many interesting research problems. This workshop is designed to foster progress in generalizable policy learning, in particular, with a focus on the tasks in the physical world, such as visual navigation, object manipulation, and autonomous driving, because these real-world tasks require complex reasoning involving visual appearance, geometry, and physics. Technically, we expect to stimulate improvement in new directions such as:

Our main targeted participants are researchers interested in applying learning methods to develop intelligent embodied agents in the physical world. More specially, 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.

Call for Papers

We invite submission to the Generalizable Policy Learning in the Physical World workshop, hosted at ICLR 2022.

Paper topics

A non-exhaustive list of relevant topics:

Submission Guidelines

Review and Selection

Timeline (11:59 PM Pacific Standard Time)

Jan 10, 2022Announcement and call for submissions
Feb 25, 2022Paper submission deadline
Mar 25, 2022Review decisions announced
Apr 15, 2022Camera ready deadline

Challenge

Please refer to the ManiSkill Challenge website for details.

Workshop Schedule

(coming soon)

Invited Speakers

listed alphabetically

Organizers

listed alphabetically

Contact

For any questions, you may contact us at iclr2022gpl@gmail.com
© 2022 Generalizable Policy Learning in the Physical World