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<span style="font-family: Helvetica; font-size: 12px;">Dear colleagues,</span></p>
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<div>We are happy to announce our RSS 2023 workshop titled "<b>Robot Representations For Scene Understanding, Reasoning and Planning</b>“, scheduled for July 10 in Daegu, Republic of Korea.</div>
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<div>We invite contributions (extended abstracts or short papers) focusing on novel advances in 3D scene understanding, predicate/affordance reasoning, high-level planning and at the boundary between these research areas.</div>
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<div><b>Workshop website</b>: <a href="https://mit-spark.github.io/robotRepresentations-RSS2023/" class="moz-txt-link-freetext" id="LPlnk417886" previewremoved="true">https://mit-spark.github.io/robotRepresentations-RSS2023/</a></div>
<div><b>Submission site</b>: <a href="https://cmt3.research.microsoft.com/robrepworkshop2023/Submission/Index" class="moz-txt-link-freetext" id="LPlnk348415" previewremoved="true">https://cmt3.research.microsoft.com/robrepworkshop2023/Submission/Index</a></div>
<div><b>Submission deadline</b>: May 22, 2023, anywhere on earth</div>
<div><b>Acceptance notification</b>: June 16</div>
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<div>For more details, see below, visit the workshop website, or contact Julian at <a href="mailto:fjulian@ethz.ch" class="moz-txt-link-freetext">fjulian@ethz.ch</a>.</div>
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<div>Kind regards,</div>
<div>Jen Jen Chung, Luca Carlone, Federico Tombari, Julian Förster</div>
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<div><b>Abstract</b></div>
<div>Robots now have advanced perception, navigation, grasping and manipulation capabilities, but how come it’s still exceedingly difficult to bring these skills together to get a robot to autonomously tidy a room? A key limiting factor is that robots still
lack the contextual scene understanding capabilities that allow humans to efficiently and compactly reason about our world and our actions within it. Metric (where) and semantic (what) representations are now common, but contextual (how) representations–how
do objects interrelate and how can a robot interact with objects to achieve the task?–are still missing. How should we formulate these representations, and crucially, how can we allow robots–embodied agents–learn and update their contextual scene understanding
from live experiences? Researchers in AI knowledge representation and reasoning as well as in the more distant field of linguistics have long grappled with similar questions. The goal of this workshop is to bring together those experts with researchers in
the fields of robot scene understanding and long-horizon planning to discuss the state of the art and uncover synergies across the currently disparate disciplines.</div>
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<div><b>Speakers</b></div>
<div>Shuran Song (Columbia University), Jiayuan Mao (MIT), Janet Wiles (The University of Queensland), Manolis Savva (Simon Fraser University), Rajat Talak (MIT), Helisa Dhamo (Huawei)</div>
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<div><b>Call for papers</b></div>
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<div>Participants are invited to submit an extended abstract or short papers (up to 4 pages in RSS format) focusing on novel advances in 3D scene understanding, predicate/affordance reasoning, high-level planning and at the boundary between these research areas.
Topics of interest include but are not limited to:</div>
<div>- Novel algorithms for spatial perception that combine geometry, semantics, and context;</div>
<div>- Approaches to learning and structuring contextual knowledge from complex sensory inputs;</div>
<div>- Techniques for reasoning over spatial, semantic, and temporal aspects for long-horizon planning;</div>
<div>- Approaches that combine learning-based techniques with geometric and model-based estimation methods; and</div>
<div>- Position papers and unconventional ideas on how to reach human-level performance in robot scene understanding, task planning and execution.</div>
<div>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.</div>
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