INTERACT: Intuitive Interaction for Robots among Humans

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Saray Bakker
Andreu Matoses Gimenez
Dr. Clarence Chen
Prof. Javier Alonso-Mora - Autonomous Multi-Robot Lab (AMR) TU Delft
Prof. Wendelin Bohmer - Key collaborator.


This project is funded the ERC Starting Grant project "Intuitive Interaction for Humans among Robots (INTERACT)".

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About the Project

This project addresses the interaction of highly automated vehicles with vulnerable road users (VRU) such as pedestrians and cyclists, in the context of future urban mobility. The project pursues an integrated approach, covering the spectrum of VRU sensing, cooperative localization, behaviour modeling and intent recognition and vehicle control. Within the AMR group we focus on the vehicle control, enabling safe and efficient autonomous driving.


Amsterdam Institute for Advanced Metropolitan Solutions (AMS).

Related Publications

Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics
S. Bakker, L. Knoedler, M. Spahn, W. Böhmer, J. Alonso-Mora. In Proc. IEEE International Symposium on Multi-Robot and Multi-Agent Systems, 2023.

Abstract: In this paper, we address the problem of real-time motion planning for multiple robotic manipulators that operate in close proximity. We build upon the concept of dynamic fabrics and extend them to multi-robot systems, referred to as Multi-Robot Dynamic Fabrics (MRDF). This geometric method enables a very high planning frequency for high-dimensional systems at the expense of being reactive and prone to deadlocks. To detect and resolve deadlocks, we propose Rollout Fabrics where MRDF are forward simulated in a decentralized manner. We validate the methods in simulated close-proximity pick-and-place scenarios with multiple manipulators, showing high success rates and real-time performance.

Social behavior for autonomous vehicles
W. Schwarting, A. Pearson, J. Alonso-Mora, S. Karaman, D. Rus. In , Proceedings of the National Academy of Sciences USA (PNAS), 2019.

Abstract: We present a framework that integrates social psychology tools into controller design for autonomous vehicles. Our key insight utilizes Social Value Orientation (SVO), quantifying an agent’s degree of selfishness or altruism, which allows us to better predict driver behavior. We model interactions between human and autonomous agents with game theory and the principle of best response. Our unified algorithm estimates driver SVOs and incorporates their predicted trajectories into the autonomous vehicle’s control while respecting safety constraints. We study common-yet-difficult traffic scenarios: highway merging and unprotected left turns. Incorporating SVO reduces error in predictions by 25%, validated on 92 human driving merges. Furthermore, we find that merging drivers are more competitive than nonmerging drivers.