Incremental Object Grounding Using Scene Graphs

Yi, John Seon Keun; Kim, Yoonwoo; Chernova, Sonia

doi:10.48550/arxiv.2201.01901

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A very recent work leverages scene graph reasoning to disambiguate human utterances when referring to various instances of the same object [31]. Their framework reasons about static domains, whereas our robot leverages scene analysis to plan towards achieving long-term goals.…”

Section: Related Workmentioning

confidence: 99%

Reasoning with Scene Graphs for Robot Planning under Partial Observability

Amiri¹,

Chandan²,

Zhang³

2022

Preprint

View full text Add to dashboard Cite

Robot planning in partially observable domains is difficult, because a robot needs to estimate the current state and plan actions at the same time. When the domain includes many objects, reasoning about the objects and their relationships makes robot planning even more difficult. In this paper, we develop an algorithm called scene analysis for robot planning (SARP) that enables robots to reason with visual contextual information toward achieving long-term goals under uncertainty. SARP constructs scene graphs, a factored representation of objects and their relations, using images captured from different positions, and reasons with them to enable context-aware robot planning under partial observability. Experiments have been conducted using multiple 3D environments in simulation, and a dataset collected by a real robot. In comparison to standard robot planning and scene analysis methods, in a target search domain, SARP improves both efficiency and accuracy in task completion. Supplementary material can be found at https://tinyurl.com/sarp22

show abstract

Section: Related Workmentioning

confidence: 99%