Luigi Palmieri

I am a group leader and research scientist at Robert Bosch GmbH Corporate Research, where I work on artificial intelligence methods that enable autonomous agents, such as autonomous driving cars and robots, to learn and generate complex behaviors.

I earned my PhD with focus on robot planning, control and learning at Uni Freiburg, Social Robotics Laboratory. I obtained my Master and Bachelor degrees in computer engineering at the Università degli Studi della Campania, Luigi Vanvitelli.

Research Interests

My wish is to commit and to contribute to the technological, social and economical progress of our society, by developing novel AI and robotics technologies that can improve and be integral part of our daily life.

In particular, I am interested in developing AI algorithms for smart autonomous decision making under uncertainty for single and multi-agent systems, such as: human behavior, activitiy and motion prediction, also using vision-language models; behavior learning in dynamic environments, reinforcement learning, hybrid systems of learning-planning-control.

Academic Service

I’m a co-organizer of the Long-term Human Motion Prediction series of workshops, organizer and associate editor of the Special Issue on Long-term Human Motion Prediction on the IEEE Robotics and Automation Letters, served as associate editor for ICRA and PC/reviewer for: ICRA, IROS, RA-L, TRO, AAAI, and various ML/AI workshops.

news

May 17, 2024	A lot of interesting discussions during our workshop LHMP at ICRA 2024. I had also the opportunity to present our recent works on context-aware robot navigation at the workshop on Supervised autonomy: how to shape human-robot interaction from the body to the brain!!
Mar 15, 2024	I had the pleasure to talk about our recent works on context-aware robot navigation at the workshop on Cooperative Autonomous Robots in Research and Industry at the European Robotics Forum 2024!!
Oct 30, 2023	We enjoyed the discussions at IROS 2023 about our latest research projects! Our novel approaches on semantic-aware navigation, multi-modal model predictive control and human-aware task and behavior planning got quite positive feedback. Fore more details check the publications section.

selected publications

Human motion trajectory prediction: A survey

Rudenko, Andrey, Palmieri, Luigi, Herman, Michael, Kitani, Kris M, Gavrila, Dariu M, and Arras, Kai O

The International Journal of Robotics Research 2020

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With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.
Distance metric learning for RRT-based motion planning with constant-time inference

Palmieri, Luigi, and Arras, Kai O

In 2015 IEEE International Conference on Robotics and Automation (ICRA) 2015

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The distance metric is a key component in RRT-based motion planning that deeply affects coverage of the state space, path quality and planning time. With the goal to speed up planning time, we introduce a learning approach to approximate the distance metric for RRT-based planners. By exploiting a novel steer function which solves the two-point boundary value problem for wheeled mobile robots, we train a simple nonlinear parametric model with constant-time inference that is shown to predict distances accurately in terms of regression and ranking performance. In an extensive analysis we compare our approach to an Euclidean distance baseline, consider four alternative regression models and study the impact of domain-specific feature expansion. The learning approach is shown to be faster in planning time by several factors at negligible loss of path quality.
Dispertio: Optimal sampling for safe deterministic motion planning

Palmieri, Luigi, Bruns, Leonard, Meurer, Michael, and Arras, Kai O

IEEE Robotics and Automation Letters 2019

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A key challenge in robotics is the efficient generation of optimal robot motion with safety guarantees in cluttered environments. Recently, deterministic optimal sampling-based motion planners have been shown to achieve good performance towards this end, in particular in terms of planning efficiency, final solution cost, quality guarantees as well as non-probabilistic completeness. Yet their application is still limited to relatively simple systems (i.e., linear, holonomic, Euclidean state spaces). In this work, we extend this technique to the class of symmetric and optimal driftless systems by presenting Dispertio, an offline dispersion optimization technique for computing sampling sets, aware of differential constraints, for sampling-based robot motion planning. We prove that the approach, when combined with PRM*, is deterministically complete and retains asymptotic optimality. Furthermore, in our experiments we show that the proposed deterministic sampling technique outperforms several baselines and alternative methods in terms of planning efficiency and solution cost.