# Semantic-level Decentralized Multi-Robot Decision-Making using Probabilistic Macro-Observations

arXiv: https://arxiv.org/abs/1703.05623 · HTML: https://mlsanity.com/paper/1703.05623/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2017.

## Built on by (most cited first, 2 of 2)

- [Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.10113/) (2022)
- [Formal Modelling for Multi-Robot Systems Under Uncertainty](https://mlsanity.com/paper/2305.17018/) (2023)

## Abstract (arXiv)

Robust environment perception is essential for decision-making on robots operating in complex domains. Intelligent task execution requires principled treatment of uncertainty sources in a robot's observation model. This is important not only for low-level observations (e.g., accelerometer data), but also for high-level observations such as semantic object labels. This paper formalizes the concept of macro-observations in Decentralized Partially Observable Semi-Markov Decision Processes (Dec-POSMDPs), allowing scalable semantic-level multi-robot decision making. A hierarchical Bayesian approach is used to model noise statistics of low-level classifier outputs, while simultaneously allowing sharing of domain noise characteristics between classes. Classification accuracy of the proposed macro-observation scheme, called Hierarchical Bayesian Noise Inference (HBNI), is shown to exceed existing methods. The macro-observation scheme is then integrated into a Dec-POSMDP planner, with hardware experiments running onboard a team of dynamic quadrotors in a challenging domain where noise-agnostic filtering fails. To the best of our knowledge, this is the first demonstration of a real-time, convolutional neural net-based classification framework running fully onboard a team of quadrotors in a multi-robot decision-making domain.