ISMR 2020 Workshop on Data-Driven Methods for Robotic Minimally-Invasive Surgery
Workshop Date: Postponed to ISMR 2021
Venue: Georgia Institute of Technology, Atlanta, GA USA
The objective of this workshop is to engage the international community interested in data-driven methods for robotic minimally-invasive surgery. The primary application domain is the telesurgical approach exemplified in operating rooms by the da Vinci surgical system and in research labs by the da Vinci Research Kit (dVRK) and Raven II open platforms. The workshop will present recent developments of infrastructure to support machine learning in robotic surgery, which includes physical infrastructure, such as phantoms, data collection infrastructure, and software environments, including simulators. One goal is to build a worldwide collaboration focused on collection and sharing of data and algorithms for machine learning in robotic minimally-invasive surgery, from projects on similar topics funded by multiple national and international agencies, including NSF in the United States, ERC in the European Union and UGC in Hong Kong. The workshop will also provide a forum for researchers to present their results in data-driven methods for scene perception, intelligent assistance, semi-autonomous teleoperation and surgical autonomy.
Hands-On Simulator Session
Attendees will have the opportunity to test out the AMBF simulator during Session 3. Interested participants should set up their laptop prior to the workshop, following the instructions provided here (TBD).
Call for Abstracts/Posters
We will solicit abstracts for poster presentation. Travel awards will be offered to selected students who have accepted posters.
|Peter Kazanzides||Blake Hannaford||Gregory S. Fischer|
|Johns Hopkins University||University of Washington||Worcester Polytechnic Institute|
|Michael Yip||Paolo Fiorini|
|Univ. of California, San Diego||University of Verona|
The following program was developed for the ISMR 2020 workshop originally scheduled for April 22, 2020. These presentations have not yet been confirmed for ISMR 2021 and are likely to change.
|08:30||Welcome and Workshop Overview||Peter Kazanzides (JHU)|
|08:45||Session 1: Infrastructure|
|08:45||Infrastructure for machine learning in minimally-invasive robotic surgery||Peter Kazanzides (JHU)|
|09:00||Collaborative Robotics Toolkit (CRTK): Open Software Framework for Surgical Robotics Research||Melody Su (UW)|
|09:15||SuPer: An Integrated Surgical Perception Framework for Endoscopic Image-guided Minimally Invasive Surgery||Michael Yip (UCSD)|
|09:30||Data-driven error correction for soft-tissue simulations||Jie Ying Wu (JHU)|
|09:45||A Proposed Architecture for Cognitive Surgical Robots: Theoretical Background and Initial Experiments||Paolo Fiorini (Univ. of Verona)|
|10:30||Session 2: Simulators (Hands-On)|
|10:30||Surgical Simulators to Support Machine Learning||Greg Fischer (WPI)|
|10:45||Hands-on Session / Tutorial||Adnan Munawar (JHU), Farid Tavakkolmoghaddam (WPI)|
|13:30||Session 3: Applications|
|13:30||Machine learning for compliant mechanism calibration||Blake Hannaford (UW)|
|13:45||Towards Resilient Cyber-Physical Systems for Robotic Surgery||Homa Alemzadeh (Univ. of Virginia)|
|14:00||Data-Driven Online Learning and Manipulation of Unmodeled Deformable Tissues and Continuum Robots||Farshid Alambeigi (Univ. of Texas, Austin)|
|14:15||Deep Learning for Calibration and Control in Robot-Assisted Surgery||Daniel Seita (Univ. of California, Berkeley)|
|14:30||Segmentation and Tracking of endoscopic and microscopic surgical Instruments||Niveditha Kalavakonda (Univ. of Washington)|
|15:30||Session 4: Posters|