# Causality-enhanced Decision-Making for Autonomous Mobile Robots in Dynamic Environments

arXiv: https://arxiv.org/abs/2504.11901 · HTML: https://mlsanity.com/paper/2504.11901/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 81 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 27 of 27)

- [Detecting causal associations in large nonlinear time series datasets](https://mlsanity.com/paper/1702.07007/) (2017)
- [DYNOTEARS: Structure Learning from Time-Series Data](https://mlsanity.com/paper/2002.00498/) (2020)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets](https://mlsanity.com/paper/2003.03685/) (2020)
- [Core Challenges of Social Robot Navigation: A Survey](https://mlsanity.com/paper/2103.05668/) (2021)
- [High-recall causal discovery for autocorrelated time series with latent confounders](https://mlsanity.com/paper/2007.01884/) (2020)
- [Principles and Guidelines for Evaluating Social Robot Navigation Algorithms](https://mlsanity.com/paper/2306.16740/) (2023)
- [Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective](https://mlsanity.com/paper/2111.14820/) (2021)
- [TH"OR: Human-Robot Navigation Data Collection and Accurate Motion Trajectories Dataset](https://mlsanity.com/paper/1909.04403/) (2019)
- [Causal Reasoning in Simulation for Structure and Transfer Learning of Robot Manipulation Policies](https://mlsanity.com/paper/2103.16772/) (2021)
- [Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios](https://mlsanity.com/paper/2302.10135/) (2023)
- [Causal discovery for time series from multiple datasets with latent contexts](https://mlsanity.com/paper/2306.12896/) (2023)
- [Causal Discovery of Dynamic Models for Predicting Human Spatial Interactions](https://mlsanity.com/paper/2210.16535/) (2022)
- [CAR-DESPOT: Causally-Informed Online POMDP Planning for Robots in Confounded Environments](https://mlsanity.com/paper/2304.06848/) (2023)
- [SOCIALGYM: A Framework for Benchmarking Social Robot Navigation](https://mlsanity.com/paper/2109.11011/) (2021)
- [A Survey of Multi-Agent Human-Robot Interaction Systems](https://mlsanity.com/paper/2212.05286/) (2022)
- [The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized](https://mlsanity.com/paper/2208.14925/) (2022)
- [MengeROS: a Crowd Simulation Tool for Autonomous Robot Navigation](https://mlsanity.com/paper/1801.08823/) (2018)
- [Using Causal Analysis to Learn Specifications from Task Demonstrations](https://mlsanity.com/paper/1903.01267/) (2019)
- [Towards a Causal Probabilistic Framework for Prediction, Action-Selection & Explanations for Robot Block-Stacking Tasks](https://mlsanity.com/paper/2308.06203/) (2023)
- [A Neuro-Symbolic Approach for Enhanced Human Motion Prediction](https://mlsanity.com/paper/2304.11740/) (2023)
- [ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction Applications](https://mlsanity.com/paper/2402.16068/) (2024)
- [Towards Probabilistic Causal Discovery, Inference & Explanations for Autonomous Drones in Mine Surveying Tasks](https://mlsanity.com/paper/2308.10047/) (2023)
- [Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios](https://mlsanity.com/paper/2406.04955/) (2024)
- [Estimating Causal Effects from Learned Causal Networks](https://mlsanity.com/paper/2408.14101/) (2024)
- [CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series](https://mlsanity.com/paper/2410.02844/) (2024)
- [Qualitative Prediction of Multi-Agent Spatial Interactions](https://mlsanity.com/paper/2307.00065/) (2023)

## Built on by (most cited first, 1 of 1)

- [Estimating the Causal Impact of Social Navigation Signals in Corridor Scenarios](https://mlsanity.com/paper/2610.16411/) (2026)

## Abstract (arXiv)

The growing integration of robots in shared environments-such as warehouses, shopping centres, and hospitals-demands a deep understanding of the underlying dynamics and human behaviours, including how, when, and where individuals engage in various activities and interactions. This knowledge goes beyond simple correlation studies and requires a more comprehensive causal analysis. By leveraging causal inference to model cause-and-effect relationships, we can better anticipate critical environmental factors and enable autonomous robots to plan and execute tasks more effectively. To this end, we propose a novel causality-based decision-making framework that reasons over a learned causal model to assist the robot in deciding when and how to complete a given task. In the examined use case-i.e., a warehouse shared with people-we exploit the causal model to estimate battery usage and human obstructions as factors influencing the robot's task execution. This reasoning framework supports the robot in making informed decisions about task timing and strategy. To achieve this, we developed also PeopleFlow, a new Gazebo-based simulator designed to model context-sensitive human-robot spatial interactions in shared workspaces. PeopleFlow features realistic human and robot trajectories influenced by contextual factors such as time, environment layout, and robot state, and can simulate a large number of agents. While the simulator is general-purpose, in this paper we focus on a warehouse-like environment as a case study, where we conduct an extensive evaluation benchmarking our causal approach against a non-causal baseline. Our findings demonstrate the efficacy of the proposed solutions, highlighting how causal reasoning enables autonomous robots to operate more efficiently and safely in dynamic environments shared with humans.