The goal of this workshop is to bring together researchers from robotics, computer vision, machine learning, and neuroscience to examine the challenges and opportunities emerging from the design of environment representations and perception algorithms that unify semantics, geometry, and physics. This goal is motivated by two fundamental observations. First, the development of advanced perception and world understanding is a key requirement for robot autonomy in complex, unstructured environments, and an enabling technology for robot use in transportation, agriculture, mining, construction, security, surveillance, and environmental monitoring. Second, despite the unprecedented progress over the past two decades, there is still a large gap between robot and human perception (e.g., expressiveness of representations, robustness, latency). The workshop aims to bring forward the latest breakthroughs and cutting-edge research on multimodal representations, as well as novel perception, infer
ence, and learning algorithms that can generate such representations.
* The workshop will include keynote presentations from established researchers in robotics, machine learning, computer vision, human and animal perception.
* There will be two panel discussions and two poster sessions highlighting contributed papers throughout the day.
* There will be a demo session including exciting live demos (best demo takes home a monetary prize - see below).
The workshop is endorsed by the IEEE RAS Technical Committee for Computer & Robot Vision.
To encourage rigorous innovative submissions, this year we plan to award a monetary prize for the best paper and the best demo presented during the workshop. Quality and impact of the submissions will be evaluated by the program committee. More details about the award will be given shortly.
Participants are invited to submit a full paper (following ICRA formatting guidelines) or an extended abstract (up to 2 pages) related to key challenges in unified geometric, semantic, topological, and physical representations, and associated perception, inference, and learning algorithms. Topics of interest include but are not limited to:
- novel representations that combine geometry, semantics, and physics, and allow reasoning over spatial, semantic, and temporal aspects
- contextual inference techniques that produce maximum likelihood estimates over hybrid multi-modal representations
- learning techniques that produce cognitive representations directly from complex sensory inputs
- approaches that combine learning-based techniques with geometric estimation methods
- position papers and unconventional ideas on how to reach human-level performance across the broad spectrum of perceptual problems arising in robotics.
Contributed papers will be reviewed by the organizers and a program committee of invited reviewers. Accepted papers will be published on the workshop website and will be featured in spotlight presentations and poster sessions. We strongly encourage the preparation of live demos to accompany the papers. We plan to select the best submissions and invite the authors of these papers to contribute to a special issue on the IEEE Transactions on Robotics, related to the topic of the workshop.
- Nikolay Atanasov, University of California, San Diego
- Luca Carlone, Massachusetts Institute of Technology
INVITED SPEAKERS (tentative):
- Jana Kosecka, George Mason University
- Dieter Fox, University of Washington
- Andrew Davison, Imperial College London
- Jitendra Malik, University of California, Berkeley
- Srini Srinivasan, Queensland Brain Institute