Javier Alonso-Mora
Associate Professor
Head of the lab
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Dr. Javier Alonso-Mora is an Associate Professor at the
Cognitive Robotics department of the
Delft University of Technology
, where he leads the
Autonomous Multi-robots Laboratory
. He is a Principal Investigator at the Amsterdam Institute for Advanced Metropolitan Solutions (
AMS Institute)
and co-founder of
The Routing Company
. He is actively involved in the Delft robotics ecosystem, including the Robotics Institute, the Transportation Institute and Robovalley.
Before joining TU Delft, Dr. Alonso-Mora was a Postdoctoral Associate at the Computer Science and Artificial Intelligence Lab (CSAIL) of the Massachusetts Institute of Technology (MIT). He received his Ph.D. degree in robotics from ETH Zurich, where he worked in the
Autonomous Systems Lab, and in partnership with Disney Research Zurich.
Dr. Alonso-Mora holds a Diploma in Engineering and a Diploma in Mathematics from the
Technical University of Barcelona (
UPC)
, where he was part of the Interdisciplinary Higher Education Centre (CFIS) and the Robotics Institute (IRI).
His main research interests are in navigation, motion planning, learning and control of autonomous mobile robots, and teams thereof, that interact with other robots and humans in dynamic and uncertain environments. He is the recipient of multiple prizes and grants, including an ERC Starting Grant (2021), the ICRA Best Paper Award on Multi-robot Systems (2019), an Amazon Research Award (2019) and a talent scheme VENI award from the Netherlands Organisation for Scientific Research (2017).
Post-Docs
Andres Fielbaum
Post-Doc
On-demand transport to improve how our cities move everyday
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My postdoc project deals with mobility systems that work on-demand and in which users are requested to share the same vehicle when this increases the global efficiency of the system. We are interested in different improvements and analyses that can be done over such systems, including how to assign requests to vehicles, optimizing the size and composition of the fleet, understanding the reliability issues that emerge, and studying some emerging opportunities of coordination with traditional public transport systems. For this, we combine techniques that come from operations research with the theory of transport systems, dealing with users' and operators' points of view.
Nils Wilde
Post-Doc
HRI and Multi-robot task assignment
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My research works on the intersection of robot motion planning, human robot interaction (HRI) and multi-robot coordination. I am interested in the coordination of multi-robot systems in uncertain, human-centered environments and how inexperienced users can define complex behaviours for autonomous mobile robots via active learning frameworks. Addressing these problems has also initiated more theoretical work on multi-objective optimization for robot planning problems.
Gang (Clarence) Chen
Post-Doc
Semantic Mapping in Dynamic Environments
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My research focuses on semantic mapping in dynamic environments.
Xinwei Wang
Post-Doc
Safety analysis models for self-driving vehicles (Joint with Prof. M. Wang)
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Longer reserch description!
Daniel Jarne
Post-Doc
(Multi-Agent) Learning and Planning for risk awareness
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I am interested in problems related to decision making under uncertainty, safety and interpretability in RL, multi-agent RL and information theory.
PhD-Students
Alvaro Serra Gómez
PhD Candidate
Motion planning for multi-robot high-level scene reasoning
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Maximilian Kronmueller
PhD Candidate
I am working on methods for routing and fleet design having the application of on-demand last-mile logistics in mind.
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My PhD project focuses on developing new methods for dynamic and stochastic vehicle routing problems, having the application of multi-vehicle on-demand last-mile delivery for retail in mind. We want to develop novel routing methods such that the desired trade-off between the fleet composition, operation cost, and system performance is achieved. This type of problems result in a complex optimization task, including stochastic and incomplete information plus needing to chose which type of vehicle should be used for each request
Max Spahn
PhD Candidate
Mobile manipulation in dynamic environments
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Longer reserch description!
Luzia Knoedler
PhD Candidate
Socially Compliant and Interactive Navigation for Mobile Manipulators in Human-centred Environments
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I am interested in combining methods from optimal control, continual learning and machine learning, specifically imitation learning, to design socially compliant
and interactive control strategies for mobile manipulators in human-centered environments.
Dennis Benders
PhD Candidate
Robust motion planning and control for autonomous aerial vehicles (Main supervisor: L. Ferranti)
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Aerial vehicles play an increasingly important role in our society. Specifically, they can be very helpful to emergency responders in search and rescue operations. In these operations, two main problems arise, including the efficient exploration of the environment and ensuring safety of the vehicle at all times. My research focuses on guaranteeing safety (i.e., collision avoidance) in an unknown static environment, given model mismatch and external disturbances. The planning and control is based on Model Predictive Control (MPC). It is in my personal interest to develop a safe motion planning and control framework that will be made open source and that will be easy to deploy on a quadrotor platform with a PX4 flight controller board, NVIDIA Jetson onboard computer and Intel RealSense camera.
Max Lodel
PhD Candidate
Human-in-the-loop and Multi-Robot Autonomous Exploration for Search and Rescue Scenarios
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I am interested in enabling autonomous robots to navigate and explore unknown environments effectively while focussing their attention to regions that the human operators of the mission are actually interested in. In this context, I am working with methods from reinforcement learning, imitation learning and optimal control.
Lasse Peters
PhD Candidate
Game-theoretic motion planning for multi-agent interaction.
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My research focuses on combining methods from optimal control,
game theory, and reinforcement learning to design control strategies
for multi-agent systems under uncertainty.
Elia Trevisan
PhD Candidate
Sampling-based MPC for motion planning of vessels in urban canals.
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My goal is to develop rule- and interaction-aware motion planning algorithms for autonomous vessels in urban canals. To achieve this, I am focusing on sampling-based MPC algorithms such as Model Predictive Path Integral control, which can leverage parallel computations to approximately solve complex discontinuos and stochastic optimal control problems in real-time.
Oscar de Groot
PhD Candidate
Safe and efficient motion planning for autonomous vehicles in urban environments.
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Autonomous vehicles are nearing real-world deployment. Currently their operation is limited
to roads where there is a clear structure and where humans have limited interaction with the
vehicle. My PhD research focuses on motion planning algorithms for self-driving vehicles in
unstructured, typically urban environments. In previous work, we showed that the probability
of collision under arbitrary distributions of human motion can be limited while planning
(e.g., including possibilities of crossing). Our current work focuses on improving the
efficiency of the planner while guaranteeing safety.
Anna Mészáros
PhD Candidate
My research focuses on probabilistic predictions of traffic participants in an environment surrounding an autonomous agent.
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Khaled Mustafa
PhD Candidate
Risk-aware motion planning for autonomous driving in urban scenarios.
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Balancing safety and efficiency when planning in crowded scenarios with uncertain dynamics is challenging. The focus of my PhD research is to account for the uncertainities in the surrounding agents' behavior predictions to generate effecient plans without compromising safety by combining optimal control with learning-based approaches.
Saray Bakker
PhD Candidate
In November 2022, I started as a PhD-student on interaction-aware motion planning for mobile robots in a dynamic environment.
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Andreu Matoses Gimenez
PhD Candidate
Working on multi agent cooperative task and motion planning.
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My research focuses on high level planning in multiagent systems accounting for interaction and cooperation between other agents and humans.
Before coming to Delft, I obtained my bachelor's degree in aerospace engineering from Universitat Politècnica de València (Spain), and a master's degree from KTH Royal institute of Technology (Sweden). After my MSc, I worked as a research engineer at KTH for Professor Dimos Dimarogonas's group.
Research Assistants
Thijs Niesten
Research Engineer
Efficient testing and reproducible modular software stacks for real robotics.
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Research on real robotics can be become a problem when multiple researchers work on the same robot with different workspaces and complicated setups.
As a research engineer, my work focuses on solving these problems by creating a time efficient, reproducible and maintainable setup for real robotics.
This mainly involves implementing and testing the setups on the
NXP HoverGames drone.
