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DTSTAMP:20250110T023312Z
LOCATION:Hall B7 (1)\, B Block\, Level 7
DTSTART;TZID=Asia/Tokyo:20241204T104500
DTEND;TZID=Asia/Tokyo:20241204T105600
UID:siggraphasia_SIGGRAPH Asia 2024_sess114_papers_464@linklings.com
SUMMARY:LLM-enhanced Scene Graph Learning for Household Rearrangement
DESCRIPTION:Technical Papers\n\nWenhao Li, Zhiyuan Yu, Qijin She, Zhinan Y
 u, Yuqing Lan, and Chenyang Zhu (National University of Defense Technology
  (NUDT)); Ruizhen Hu (Shenzhen University (SZU)); and Kai Xu (National Uni
 versity of Defense Technology (NUDT))\n\nThe household rearrangement task 
 involves spotting misplaced objects in a scene and accommodate  them with 
 proper places. It depends both on common-sense knowledge on the objective 
 side and human user preference on the subjective side. In achieving such t
 ask, we propose to mine object functionality with user preference alignmen
 t directly from the scene itself, without relying on human intervention. T
 o do so, we work with scene graph representation and propose LLM-enhanced 
 scene graph learning which transforms the input scene graph into an afford
 ance-enhanced graph (AEG) with information-enhanced nodes and newly discov
 ered edges (relations). In AEG, the nodes corresponding to the receptacle 
 objects are augmented with context-induced affordance which encodes what k
 ind of carriable objects can be placed on it. New edges are discovered wit
 h newly discovered non-local relations. With AEG, we perform task planning
  for scene rearrangement by detecting misplaced carriables and determining
  a proper placement for each of them. We test our method by implementing a
  tiding robot in simulator and perform evaluation on a new benchmark we bu
 ild. Extensive evaluations demonstrate that our method achieves state-of-t
 he-art performance on misplacement detection and the following rearrangeme
 nt planning.\n\nRegistration Category: Full Access, Full Access Supporter\
 n\nLanguage Format: English Language\n\nSession Chair: Kai Wang (Amazon)
URL:https://asia.siggraph.org/2024/program/?id=papers_464&sess=sess114
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