Acquiring generalizable manipulation policies through data-driven, learning-based approaches is a fundamental problem in robotics. Despite advancements in this field, there are many unresolved questions that have resulted in a variety of, sometimes conflicting, paradigms. This workshop is designed to bring together researchers and practitioners from a diverse range of disciplines, robotics, vision, graphics, machine learning to share their insights and perspectives, with the goal of advancing interdisciplinary collaboration and promoting the development of innovative, new paradigms in generalizable manipulation policy learning. More specifically, we aim to discuss questions that are important for the future development of this field, including but not limited to:
Our targeted participants are researchers and engineers who are interested in the field of robotics and embodied AI, and have a background in machine learning, computer vision, computer graphics, natural language processing or related areas. The workshop will also be of interest to practitioners in industry who are working on the development of intelligent robots and other related systems.
In affiliation to this workshop, we are also organizing the ManiSkill2 Challenge, which is a large-scale robotic manipulation challenge that seeks to benchmark generalizable robotic agents that can interact with the 3D world. We will announce winners and host winner presentations in this workshop.
|Start Time (KST, UTC+9)||Event||Chair|
|9:00||Welcome and introduction||Rui Chen|
Andy Zeng: From words to actions
Xingyu Lin: Generalizable Manipulation with Large Internet Data and Small Robot Data
|10:15||Coffee break & Poster session|
Shan Luo: Empowering Robotic Manipulation: Bridging Sim-to-Real with Tactile Representations
Yuzhe Qin: Learning Generalizable Dexterous Manipulation from Vision, Touch and Human Demonstration
Georgia Chalvatzaki: Real robots learn with structure
Joseph J. Lim: Skill-based Robot Learning
Siddhant Haldar: Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations
Moritz Reuss: Goal-Conditioned Imitation Learning using Score-based Diffusion Policies
Pietro Mazzaglia: World Models for Robotic Manipulation
|15:30||Award ceremony of the ManiSkill2 challenge||Jiayuan Gu|
|15:40||Winner presentation: Xuetao Li||Jiayuan Gu|
|16:00||Panel discussion||Jiayuan Gu|
1st Prize: Team GXU-LIPE, Guangxi University
2nd Prize: Team baochen, Shanghai Jiaotong University
3rd Prize: Team dee, Hong Kong Polytechnic UniversityPlease refer to the ManiSkill2 Challenge website for details.