# Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios

arXiv: https://arxiv.org/abs/2406.04955 · HTML: https://mlsanity.com/paper/2406.04955/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [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)
- [Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets](https://mlsanity.com/paper/2003.03685/) (2020)
- [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 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)
- [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)
- [Qualitative Prediction of Multi-Agent Spatial Interactions](https://mlsanity.com/paper/2307.00065/) (2023)

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

- [Exploring Causality for HRI: A Case Study on Robotic Mental Well-being Coaching](https://mlsanity.com/paper/2503.11684/) (2025)
- [Causality-enhanced Decision-Making for Autonomous Mobile Robots in Dynamic Environments](https://mlsanity.com/paper/2504.11901/) (2025)
- [Estimating the Causal Impact of Social Navigation Signals in Corridor Scenarios](https://mlsanity.com/paper/2610.16411/) (2026)

## Abstract (arXiv)

Deploying robots in human-shared environments requires a deep understanding of how nearby agents and objects interact. Employing causal inference to model cause-and-effect relationships facilitates the prediction of human behaviours and enables the anticipation of robot interventions. However, a significant challenge arises due to the absence of implementation of existing causal discovery methods within the ROS ecosystem, the standard de-facto framework in robotics, hindering effective utilisation on real robots. To bridge this gap, in our previous work we proposed ROS-Causal, a ROS-based framework designed for onboard data collection and causal discovery in human-robot spatial interactions. In this work, we present an experimental evaluation of ROS-Causal both in simulation and on a new dataset of human-robot spatial interactions in a lab scenario, to assess its performance and effectiveness. Our analysis demonstrates the efficacy of this approach, showcasing how causal models can be extracted directly onboard by robots during data collection. The online causal models generated from the simulation are consistent with those from lab experiments. These findings can help researchers to enhance the performance of robotic systems in shared environments, firstly by studying the causal relations between variables in simulation without real people, and then facilitating the actual robot deployment in real human environments. ROS-Causal: https://lcastri.github.io/roscausal