arXiv subjects

Changxuan Yao

Publications and source records attributed to Changxuan Yao.

2 recordsLinked to original sources

arXiv2026

ATOM: Abstract Transition and Option Modeling - Experience-Informed Hierarchical Navigation

We propose ATOM (Abstract Transition and Option Modeling), a hierarchical navigation framework that combines higher-level graph planning with reusable lower-level option policies for efficient repeated navigation in large-scale 3D environments represented by hierarchical structures such as 3D scene graphs (3DSGs). ATOM operates on a hierarchical graph representation, implemented using, for instance, the Rooms and Places layers of a 3DSG, where each directed abstract transition is associated with a navigation option. For each transition, ATOM computes and stores a reusable option policy along with its state-dependent cost-to-go. Across repeated navigation tasks, ATOM maintains a smoothed empirical distribution over option initiation states and uses it to progressively refine the corresponding abstract transition cost. Ablation studies show that ATOM reduces cumulative repeated-planning time by 97.5% relative to a monolithic MDP baseline while maintaining trajectories within approximately 7% of a Global Places A* shortest-path reference. Experience-informed refinement further reduces abstract transition-cost estimation error by 10.3%. Physical experiments validate ATOM on an F1Tenth vehicle, successfully completing all five navigation queries with 43% of inter-region option executions reusing previously generated solutions.

cs.RO↗

arXiv2026

Mobility-Budget-Constrained Submodular Maximization for Distributed Semantic Multi-Robot Exploration

Multi-robot exploration under communication and mobility constraints requires balancing information gain with reachability. We present a distributed semantic exploration framework that combines Dirichlet mapping, budget-constrained trajectory selection, and Resource-Aware distributed Greedy (RAG) coordination. Each robot clusters frontiers, computes terrain- and heading-aware least-cost paths with a single budget-limited Dijkstra search, and selects feasible paths by marginal information gain, represented by the expected reduction in Dirichlet entropy. Each robot's per-round selection is exact, the team's joint per-round information gain is at least half the centralized optimum, and the bound degrades gracefully under limited communication. Ten-run gridworld ablations show improved coverage, semantic mIoU, and map certainty with lower entropy and traversal cost per unit distance than shortest-distance frontier selection. Budget-sensitivity experiments show that an intermediate budget best balances path reach and receding-horizon execution. In photorealistic full-system simulation against five coordination rules run in their native published forms, the proposed method attains the highest mean coverage and semantic accuracy while being the only one that respects the per-trajectory mobility budget.

cs.RO↗