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This paper addresses the computational bottleneck of using large scene graphs in robotic planning by formalizing "sufficiency" through an information-spaces framework. It introduces a method to merge and prune graph nodes while guaranteeing that plans generated on the reduced model remain feasible in the full environment. The work advances the theoretical foundations for scalable, task-aware robot navigation and manipulation systems.
An Information-Space Perspective to Scene Graph Sufficiency for Robotic Task Planning
arXiv:2609.15587v1 Announce Type: new Abstract: Planning in complex environments requires task specifications grounded in representations that capture objects, relations, and affordances; scene graphs meet this need, but their size in large environments hinders efficient planning. While task-aware pruning and hierarchical abstractions have been explored, a general, task-centric formalization of what constitutes a sufficient scene graph for planning remains open. This paper provides such a formalization by modeling planning over scene graphs within an information-spaces framework through the definition of scene graph transition systems and relevant action semantics for navigation and manipulation. We then introduce derived scene graphs via information mappings that merge and prune nodes and induce quotient transition systems augmented with motion primitives to capture higher-level actions over merged graph nodes. Sufficiency is characterized by two conditions: (i) the information mapping yields a deterministic quotient, and (ii) the task is well-posed over derived traces, ensuring plans found on the derived model are feasible on the maximal system. We illustrate the framework using a task over an example environment, showing both sufficient and insufficient reduced scene graphs.
Abstract:Planning in complex environments requires task specifications grounded in representations that capture objects, relations, and affordances; scene graphs meet this need, but their size in large environments hinders efficient planning. While task-aware pruning and hierarchical abstractions have been explored, a general, task-centric formalization of what constitutes a sufficient scene graph for planning remains open. This paper provides such a formalization by modeling planning over scene graphs within an information-spaces framework through the definition of scene graph transition systems and relevant action semantics for navigation and manipulation. We then introduce derived scene graphs via information mappings that merge and prune nodes and induce quotient transition systems augmented with motion primitives to capture higher-level actions over merged graph nodes. Sufficiency is characterized by two conditions: (i) the information mapping yields a deterministic quotient, and (ii) the task is well-posed over derived traces, ensuring plans found on the derived model are feasible on the maximal system. We illustrate the framework using a task over an example environment, showing both sufficient and insufficient reduced scene graphs.
Robotics (cs.RO)
arXiv:2609.15587 [cs.RO]
(or arXiv:2609.15587v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.15587 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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