In recent years, the advances in machine learning for vision and control applications support the
increasing demands for mobile robots. Such demands surge especially in the past several
months, arguably due to the spread of COVID-19. While mobile robots are typically expected to
work in controlled environments (e.g., supply chain automation at factories), more challenging
unconstrained situations (e.g., cleaning, sanitizing, etc.) have also begun attracting attention,
which in turn causes automation and safety of mobile robots to become a serious concern.
To enable mobile robots to meet such demands, equipping them with satisfactory vision and control capabilities is necessary and has become the key focus of relevant robotic research endeavors. Many sophisticated computer vision / machine learning / robotics approaches have been developed to meet this aim, including but not limited to semantic segmentation, optical flow estimation, depth estimation, object detection and tracking, domain adaptation, sim-to-real transfer, reinforcement learning and imitation learning for robot navigation. However, these advances have not yet been properly translated to significant progress in practical mobile robot applications due to the insufficiency of effective data samples from the real world, leading to unsatisfactory performance and safety concerns of mobile robots during deployment.
Moreover, effectively collecting data and efficiently utilizing them for training vision and control models, especially in unconstrained outdoor environments, have further raised a number of fundamental challenges for mobile robot applications. These challenges include several open but crucial issues, such as multimodal sensing, privacy issues, human activity recognition and prediction, as well as the constraints on batteries, computing capabilities, and limited field of view.
To better understand the aforementioned issues and improve the current solutions, this MRVC workshop presents a timely opportunity to bring together researchers in computer vision, machine learning, and robotics communities together to discuss the unique challenges and opportunities for mobile robots.
* The time zone shown in this website is in UTC+08:00
14 : 00 PM - 14 : 05 PM
Keynote (I): Dr. Simon See - Senior Director, NVIDIA AI Technology Center
AI and Simulation
14 : 05 PM - 14 : 30 PM
Keynote (II): Dr. Yoko Sasaki - Senior Researcher at National Institute of Advanced Industrial Science and Technology, Japan
Autonomous mobile robot in human living space
14 : 35 PM - 15 : 00 PM
Keynote (III): Dr. Shang-Hong Lai - Professor at NTHU and Principal Researcher at Microsoft
Deep Multimodal Learning for Face Recognition
15 : 05 PM - 15 : 30 PM
Time-Constrained Multi-Agent Path Finding in Non-Lattice Graphs with Deep Reinforcement Learning
15 : 30 PM - 15 : 45 PM
Sim-to-Real: Virtual Guidance for Robot Navigation
15 : 48 PM - 16 : 00 PM
Less Reward is More: Improved Reinforcement Learning Control of a Mobile Manipulator using Clamped Joints
16 : 03 PM - 16 : 15 PM
A Fine-grained Dynamic Inference Architecture for Semantic Image Segmentation
16 : 18 PM - 16 : 30 PM
16 : 30 PM - 16 : 35 PM
Unofficial post-workshop discussion session.
16 : 35 PM - 17 : 30 PM
Please submit papers via CMT.
At MRVC-21, we will solicit contributions at the intersection of mobile robotics and machine learning and computer vision. Specific topics of interest will include:
All submissions should be in the ACML-21 format with a maximum of eight pages for short papers and four pages for extended abstracts, including the reference and appendices. Both unpublished and already-published works are welcome. Reviews will be single-blind. Accepted papers will be published on the workshop website and presented in the form of either short talks or posters. Please note that we will NOT publish any official proceedings so that participants can submit their work to future conferences based on the feedback from the workshop.
Please submit papers via CMT: CMT link
* The time zone shown in this website is in UTC+08:00
|Submission begins for the short papers and extended abstracts||12 September 2021|
|Submission closes for the short papers extended abstracts||15 October 2021|
|Review period begins||15 October 2021|
|Review period ends||25 October 2021|
|Discussion of paper acceptance by the committee members||To be announced|
|Notification of paper acceptance||To be announced|
|Deadline for camera ready manuscripts and the posters||To be announced|
|Workshop date||17 November 2021|
We will invite keynote speakers from both academia and industry.
The organizers of this workshop contain professionals and experts from both academia and
industry (National Tsing Hua University, Tokyo Institute of Technology, Keio University,
OMRON SINIC X, NVIDIA Corporation, Microsoft, and DeepMind).
Chun-Yi Lee is an Associate Professor of Computer Science at National Tsing Hua University (NTHU), Hsinchu, Taiwan, and is the supervisor of Elsa Lab. Prof. Lee’s research focuses on deep reinforcement learning (DRL), intelligent robotics, computer vision (CV), and parallel computing systems.
Ryo Yonetani is a principal investigator at OMRON SINIC X, Japan and a project assistant professor at Keio University. His research interests include human activity recognition, visual forecasting, federated learning, and transfer learning.
Asako Kanezaki is an associate professor at Tokyo Institute of Technology. Her research interests include object detection, 3D shape recognition, and robot applications.
Mohammadamin Barekatain is a Research Engineer at DeepMind, UK. His research interests include reinforcement learning, computer vision, and algorithmic reasoning.
Simon See is currently the Solution Architecture and Engineering Director and Chief Solution Architect for Nvidia AI Technology Center. His research interests are in the area of High-Performance Computing, Big Data, Artificial Intelligence, Machine Learning, Computational Science, Applied Mathematics and Simulation Methodology.
Shang-Hong Lai is a professor at National Tsing Hua University (NTHU), Taiwan, and is a principal researcher at Microsoft AI R&D Center. Dr. Lai’s research interests are mainly focused on computer vision, image processing, and machine learning.
We will select between Zoom (for keynote and oral presentations), Gather.Town (oral and/or poster presentations), Teams, Slack, or Discord (asynchronized text-based discussions) as our platform. These services have great interfaces for desktops, smartphones, and tablets, and have been commonly used for virtual conferences and our daily online discussions. We believe that using them in combination will be ideal to make the workshop easily accessible to diverse attendees.
Accepted papers and posters will also be made available on our workshop website. The workshop presentations will be video recorded and made public during and/or after the workshop.
The MRVC workshop views diversity and inclusion seriously. We will design the workshop to be inclusive for diverse attendees. Although the workshop will be held at the time period allocated by ACML, we would also ask speakers and participants about their preferred time slots when they can easily participate based on their own time zone. We will actively utilize text-based communications among participants (e.g., chat functions in Zoom and Gather.Town, or slack/discord channels created for the workshop) so that participants whose native language is not English can use translation tools to participate in the discussion without difficulty, anytime in synchronized and asynchronized fashions. In addition, the workshop will be video recorded, which will be put on YouTube or other open streaming platforms to promote knowledge sharing for the public. The contents (i.e., papers, posters, and videos) will be put on the workshop website. The potential attendees who are unable to attend the workshop will still have access to the workshop contents afterwards.