AIMRWS21: RSS Workshop on Advancing Artificial Intelligence and Manipulation for Robotics: Understanding Gaps, Industry and Academic Perspectives, and Community Building RSS N/A, PA, United States, July 13, 2021 |
Conference website | https://sites.google.com/view/rss-ai-manipulationperspective/ |
Submission link | https://easychair.org/conferences/?conf=aimrws21 |
Abstract registration deadline | July 1, 2021 |
Submission deadline | July 1, 2021 |
Robotics: Science and Systems (R:SS), July 12-16, 2021
https://sites.google.com/view/rss-ai-manipulationperspective/
Important Dates
Extended abstracts due: June 20th July 1st
Notification of acceptance: July 1st July 6th
Final posters due: July 11th
Workshop date: July 13, 2021
Motivation and Objectives
Advances in machine learning (ML), the design of novel end-effectors and sensors, and the development of intelligent perception, planning and control algorithms have resulted in undeniable progress in robot manipulation. Impressive demonstrations of multi-fingered dexterity in research labs show that robots are increasingly capable of manipulating the physical world around them. Despite this considerable progress, however, current robotic manipulation skills remain far from human-level versatility. Moreover, academic progress in machine learning and manipulation has not translated into industry; industrial manufacturing and manipulation systems instead operate in highly structured environments and rely on simple perception, planning, and control.
In this workshop, we will bring together experts from academia and industry to identify industrial manufacturing and manipulation problems that can most benefit both from recent academic progress and advances in artificial intelligence, and in order to determine meaningful research directions that are motivated by these applications. In particular, the goals of the workshop are:
- Determining important criteria for industrial adoption and the gap between these criteria and the current approaches to manipulation in academia.
- Raise awareness of the need for ML metrics, evaluations, and benchmarks for manufacturing-relevant parts, operations, and environments requiring robots.
- Convene stakeholders to define common language for discussing ML performance, characteristics, applicability and/or tools and measurement science necessary to advance the state of ML in manufacturing robotics and reduce the risk of adopting ML-based technologies.
- Form an ongoing community to develop, review, test, mature, and contribute to the concepts and tools that can help advance the field and foster well-informed, successful adoption and implementation of ML-based manufacturing robotics capabilities.
The workshop will host talks by invited speakers, peer-reviewed poster sessions, panel discussions, and open discussions. In particular, we will include talks from representatives in different industries, including manufacturing, warehouse automation, automotive, electronics assembly, and homecare, to better understand their diverse requirements and needs. We expect insightful discussions between experts from industry and academia, leading to a better understanding of avenues for potential collaboration between the two communities.
Submissions
We invite extended abstract submissions, optionally accompanied by a video. All submissions will be reviewed (single blind) by the program committee.
- Please use the RSS template: LaTeX
- Maximum: 2 pages (excluding references)
- Optional: To provide a video please upload to a third-party hosting platform (YouTube, Dropbox, Google Drive, etc.) and add a link to the abstract
For the final presentation a poster will be required.
Organizers
Clemens Eppner (NVIDIA)
Neel Doshi (MIT)
Megan Zimmerman (NIST)
Diego Romeres (MERL)
Siyuan Dong (UW)
Devesh Jha (MERL)
Prof. Alberto Rodriguez (MIT)
Michel Beyer (ETH Zürich)
Coline Devin (Deepmind)
Arsalan Mousavian (NVIDIA)
Jingyi Xu (TU München)
Andy Zeng (Google AI)
Craig Schlenoff (NIST)
Holly Yanco (UML)
Adam Norton (UML)
Vinh Nguyen (NIST)
Siddarth Jain (MERL)