# Active Semantic Perception

arXiv: https://arxiv.org/abs/2510.05430 · HTML: https://mlsanity.com/paper/2510.05430/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2025.

## Built on by (most cited first, 3 of 3)

- [Bridging Values and Behavior: A Hierarchical Framework for Proactive Embodied Agents](https://mlsanity.com/paper/2604.27699/) (2026)
- [Fixed External Cameras as Common Prior Maps for Active 3D Scene Graph Generation](https://mlsanity.com/paper/2605.18184/) (2026)
- [RGB-only Active 3D Scene Graph Generation for Indoor Mobile Robots](https://mlsanity.com/paper/2605.18197/) (2026)

## Abstract (arXiv)

We develop an approach for active semantic perception, which refers to using the semantics of the scene for tasks such as exploration. We build a compact, multi-layer scene graph that can represent large, complex indoor environments at various levels of abstraction, e.g., nodes corresponding to rooms, objects, walls, windows etc., as well as fine-grained details of their geometry. We develop a procedure based on large language models (LLMs) to sample new plausible scene graphs of unobserved regions that are consistent with partial observations of the scene. We develop a procedure to compute the information gain of a potential waypoint upon this scene graph to enable sophisticated spatial reasoning: for example, of the two doors that lead out of the living room, one probably leads to the kitchen and the other to the bedroom. We evaluate our approach in realistic 3D indoor apartments in simulation and also on a Unitree Go 2 robot in the real world. Qualitative and quantitative analysis shows that our approach can pin down high-level and low-level semantic information in the environment quickly and more accurately than existing approaches.