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Dear colleagues,
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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">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">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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