[CfP] Workshop: Policy Learning in Geometric Spaces at IROS 2023

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Vien Ngo

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Jun 7, 2023, 9:46:09 AM6/7/23
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Dear Colleagues,  

We are pleased to invite submissions to the "Policy Learning in Geometric Spaces" workshop at the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023). The workshop will be held in-person on October 1st, 2023, in Detroit, MI, USA. 

Our Call for Papers can also be found at: https://sites.google.com/view/iros23-policy-learning 

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Important Dates 

  • Paper Submissions Open: May 1st, 2023 
  • Paper Submission Deadline: July 5th, 2023 
  • Author Notification: July 26th, 2023 
  • Camera Ready: August 10th, 2023 
  • Workshop: October 1st, 2023 

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Submission Guidelines 

We welcome two types of submissions: short papers (up to 4 pages + references) and extended abstracts (up to 2 pages + references). All submissions should be in PDF format and follow the IROS conference paper template. Please submit your papers through CMT until July 5th, 2023 (all information is available on the workshop website). All submissions will be peer-reviewed, and accepted papers will be presented as posters as well as selected papers as spotlight talks. 

The contributed papers will be made available on the workshop’s website. However, this does not constitute an archival publication and no formal workshop proceedings will be made available, meaning contributors are free to publish their work in archival journals or conferences. 

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Abstract  

The field of robotics is constantly evolving and demands robots to operate effectively in complex, 3D environments. A critical requirement for this is the ability of robots to learn policies for tasks such as grasping, manipulation, motion planning, and more. In recent years, reinforcement learning has emerged as a promising approach for policy learning in 3D geometric spaces. Specifically, researchers have focused on the use of point clouds, neural representations of geometry (NeRFs, neural SDFs, or occupancy networks), occlusion maps, and other techniques to achieve this. The persistent challenge in integrating geometric representation in policy learning lies in effectively bridging the gap between high-dimensional and low-dimensional representations of complex spatial environments. To encourage knowledge sharing and foster collaborations, this workshop aims to bring together researchers and practitioners working in this area to discuss the latest developments and identify challenges. Through this collaborative effort, we hope to further advance the field of robotics and reinforce the importance of policy learning in geometric spaces. 

 

Scope and Topics of Interest 

 

The aim of this workshop is to bring together researchers and practitioners in the field of policy learning in geometric spaces. We welcome contributions on the following topics: 

  • Representation learning techniques for graphs, meshes, or voxels 
  • Deep metric learning for vision-based policy learning in complex 3D environments 
  • Utilization of point clouds, NeRFs, neural SDFs, occupancy networks, those based on vision transformers and other advanced representations for policy learning 
  • Techniques such as occlusion maps for handling partial observability in policy learning 
  • Deep reinforcement learning algorithms for policy learning in 3D geometric spaces 
  • Equivariant algorithms for policy and model learning 
  • Identifying and addressing the challenges and opportunities of policy learning in 3D geometric spaces 
  • Real-world implementation and evaluation of policy learning algorithms in 3D environments 
  • Applications of policy learning in robotics, including grasping, manipulation, motion planning, and autonomous navigation 


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Confirmed Invited Speakers 

  • Xiaolong Wang, Assistant professor at UC San Diego in the ECE department 
  • Pete Florence, Senior Research Scientist at Google Research (Tentative) 
  • He Wang, Assistant Professor in the Center on Frontiers of Computing Studies (CFCS) at Peking University 
  • Young Min Kim, Associate Professor at Seoul National University 
  • Animesh Garg, Assistant Professor of Computer Science at University of Toronto and a Faculty Member at the Vector Institute 

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Organizing Committee 

  • Fabian Otto, PhD student at the Bosch Center for Artificial Intelligence (BCAI) 
  • Ning Gao, PhD at the Bosch Center for Artificial Intelligence (BCAI) 
  • Nicolas Schreiber, PhD student in the Autonomous Learning Robots group at KIT 
  • Vien Ngo, Research scientist at the Bosch Center for Artificial Intelligence (BCAI) 
  • Danny Driess, Doctoral researcher at TU Berlin 
  • Gerhard Neumann, Full professor at the KIT and heading the chair "Autonomous Learning Robots" 
  • Clemens Eppner, Research Scientist in the Seattle Robotics Lab at NVIDIA Research 
  • Georgia Chalvatzaki, Full Professor at TU Darmstadt 

 
For any inquiries or questions, please contact us at fabia...@bosch.com. We look forward to your contributions and participation in the workshop!


Mit freundlichen Grüßen / Best regards

Vien NgoReinforcement learning and planning (CR/AIR4.1)Robert Bosch GmbH | Postfach 10 60 50 | 70049 Stuttgart | GERMANY | [www.bosch.com]www.bosch.com

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